JP4470311B2 - Travel pattern generator - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a running pattern production device producible of strict balance schedule of energy. SOLUTION: A running pattern production device 1 produced the running pattern of the self-car in a planed running path by using information obtained with a road information obtaining means 11 obtaining information related with the planned running path. By using the information obtained with the road information obtaining means 11, stop times of the self-car in the planned running path is predicted with the stop times prediction means 13 for predicting the stop times of the self-car.

Description

【００６８】
次に、一つの区間内で停止する信号機または交差点の選択方法について説明する。
まず、直進する交差点については、１２車線道路との交差点、２１車線道路との交差点といった優先順位に従って停止場所を選択する。また、料金所、右折（左側通行の場合）、より優先度の高い道路への右左折（細街路→１車線道路、１車線道路一２車線道路）の場合は、予測停止回数にかかわらず必ず停止すると予測する。
【００６９】
さて、図２に示す経路を例に挙げて、停止する交差点の選択について説明すると、表１より出発地から交差点Ａまでの区間の予測停止回数は２．６であり、小数点以下を切り上げて３回停止すると予測する。そして、この停止場所として、１車線道路から２車線道路へ左折する交差点Ａ、２車線道路との交差点であるＡ１、および１車線道路との交差点であるＡ２を選択する。
【００７０】
同様に、交差点ＡからＢまでの区間での予測停止回数は表１より１．１であり、小数点以下を切り捨てて１回停止すると予測する。しかし、この区間には「必ず停止する」と予測することとした料金所Ｂ２と右折する交差点Ｂがあるので、予測停止回数は１回であるが、料金所Ｂ２と交差点Ｂの２個所で停止すると予測する。
【００７１】
また、過去に走行したことがある区間の場合には、そのときの走行履歴を用いて、停止確率（停止回数／通った回数）の高い交差点または信号機から順に停止場所を選択するように予測しても良い。
【００７２】
ステップＳ１４では、ステップＳ１１において収集した情報を用いて走行速度を予測する。走行速度（Ｖｓ［ｋｍ／ｈ］）の予測方法の一例としては、以下の式２に示すように道路の種類とカーブの曲率を用いる方法が考えられる。ここで、Ｖｃｒｖ（ｒ）は、例えば図４に示すような道路の曲率半径ｒ［ｍ］についての関数である。
【００７３】
【数２】
Ｖｓ＝Ｖｃｒｖ（ｒ）＋Ｔｓ …式２
【００７４】
ただし、Ｔｓは走行予定経路が細街路のときは−１０、１車線道路のときは０、２車線道路のときは１０、高速道路のときは４５とする。
【００７５】
ステップＳ１５では、ステップＳ１１において収集した情報を用いて加減速度を予測する。ここで、加速度（Ａｃｃ［ｍ／ｓ^２ ］）および減速度（Ｄｅｃ［ｍ／ｓ^２ ］）の予測方法の一例として、下記式３および式４に示すような道路種別を用いる方法がある。
【００７６】
【数３】
Ａｃｃ＝０．７６＋Ｔａ …式３
【００７７】
ただし、Ｔａは、走行予定経路が細街路または１車線道路のときは０、２車線道路のときは−０．０３、高速道路の導入路のときは０．１５、高速道路の本線のときは−０．０３とする。
【００７８】
Ｄｅｃ＝−１．１１＋Ｔｄ …式４
ただし、Ｔｄは、走行予定経路が細街路または１車線道路のときは０、２車線道路のときは−０．１４、高速道路の導入路のときは０．０３、高速道路の本線のときは０．３６とする。
【００７９】
以下にステップＳ１４およびＳ１５における式２から式４を用いて、図２に示す経路上での走行速度、加速度および減速度を予測した結果を表２に示す。
【００８０】
【表２】

【００８１】
次に、ステップＳ１６では、ステップＳ１３からＳ１５において予測した停止回数、走行速度、加減速度を組み合せて各区間ごとに走行パターンの予測波形を生成する。上記表１および表２に示した図３の経路上における停止回数および走行速度、加速度、減速度の予測結果に従って生成した走行パターンを図５に示す。
【００８２】
ところで、こうした走行パターンをハイブリッド車のバッテリ充放電スケジュールの決定などの車両制御に用いる場合は、時間軸を基準にしている方が都合がよい。図５に示す距離軸から図６に示す時間軸への変換方法を以下に説明する。
【００８３】
図６に示すように、加速時および減速時に進む距離をＳａ［ｍ］およびＳｄ［ｍ］、定速走行時に進む距離をＳｓ［ｍ］、発進から停止までに進む距離（交差点間の距離）をＬ［ｍ］とすると、以下の式が成り立つ。
【００８４】
【数４】
Ｌ＝Ｓａ＋Ｓｓ＋Ｓｂ …式５
【００８５】
ここで、ＳａおよびＳｄは、走行速度Ｖｓ、加速度Ａｃｃ、減速度Ｄｅｃを用いて以下のように表せる。
【００８６】
【数５】
Ｓａ＝（Ｖｓ／３．６）^２ ／２Ａｃｃ …式６
Ｓｄ＝（Ｖｓ／３．６）^２ ／２Ｄｅｃ …式７
【００８７】
また、定速走行する時間Ｔｓ［ｓｅｃ］は以下のように表せる。
【００８８】
【数６】
Ｔｓ＝Ｓｓ／（Ｖｓ／３．６） …式８
【００８９】
以上の式６から式８を式５に代入すると、Ｔｓは以下のようにして求められる。
【００９０】
【数７】
Ｔｓ＝Ｌ／（Ｖｓ／３．６）
−（Ｖｓ／３．６）（１／Ａｃｃ−１／Ｄｅｃ）／２ …式９
【００９１】
こうして走行速度、加速度および減速度の予測値から加速時間、定速走行時間、減速時間を定めて、時間軸を基準にした走行パターンを生成することができる。
【００９２】
最後にステップＳ１７では、目的地までの全区間の走行パターン予測が終了したかどうかを判定する。
【００９３】
このように、本実施形態の走行パターン生成装置１によれば、目的地までの経路の道路種別やカーブの曲率、交差点や信号機の位置に関する情報を用いて走行速度、加速度、減速度、停止回数を予測するので、特に交差点および信号機の平均間隔を用いて停止回数を予測することにより、道路状態を考慮した走行パターンを生成することができる。
【００９４】
また、予測停止回数の小数点以下の数字の処理を、時間帯に応じて、日中は四捨五入し、交通量の多い朝夕のラッシュ時は小数点以下の切り上げ数を４以下に下げ、交通量の少ない深夜や早朝は小数点以下の切り上げ数を６以上に上げるようにすることにより、交通状況により適応した停止回数を予測できる。
【００９５】
また、右折する交差点や、より優先度の高い道路へ右左折する交差点、または料金所では、その場所で必ず停止すると予測するので、実際の運転により適合した停止回数の予測ができる。
【００９６】
さらに、直進する交差点については、より優先度の高い道路との交差点で停止すると予測するので、この点においても実際の運転により適合した停止回数の予測ができる。
【００９７】
また、過去に走行したことがある区間では、過去の走行履歴を用いて、停止確率｛（停止回数）／（通った回数））の高い交差点または信号機から順に停止場所を選択するので、実際の運転により適合した停止回数の予測ができる。
【００９８】
第２実施形態
次に、本発明の第２実施形態について説明する。
本実施形態の制御内容は、図７に示したものと基本的には同じであるが、ステップＳ１６において渋滞区間の走行パターンの生成が加わる点が異なる。また、図８に示す交通状況の変化に対応するための走行パターンの再予測についての処理が追加される。
【００９９】
以下、上述した第１実施形態と共通の制御処理については説明を省略し、ステップＳ１６で行われる処理を説明する。ステップＳ１６では、ナビゲーション装置を横成しているＶＩＣＳ受信機にて目的地までの経路上に渋滞情報を受信した場合には、一般道路の場合には渋滞区間における停止回数を次のように予測する。
【０１００】
すなわち、渋滞中の走行パターンのモデルを図９に示すが、ここではある一定速度に到達するまで加速したのち、すぐに減速し始めて停止することを繰り返すものとする。図９に示す走行パターンの速度の最大値を、ＶＩＣＳセンターから送信されてくる渋滞区間の平均走行速度（Ｖｊ［ｋｍ／ｈ］）にある一定値（例えば２）を乗じた値とすると、一回の発進から停止までに進む距離（ｌｊ［ｍ］）は以下のようになる。
【０１０１】
【数８】
ｌｊ＝２Ｖｊ^２ （１／Ａｃｃ＋１／Ｄｅｃ） …式１０
【０１０２】
よって、渋滞区間の長さをＬ［ｍ］とすると、渋滞区間での停止回数はＬ／ｌｊ［回］となる。また、停止から次の発進までの平均時間間隔Ｔｊ［ｓｅｃ］は以下のようになる。
【０１０３】
【数９】
Ｔｊ＝（Ｌ／Ｖｊ）／（Ｌ／ｌｊ）＝ｌｊ／Ｖｊ …式１１
【０１０４】
以上で求めた停止回数と停止から次の発進までの平均時間間隔Ｔｊとに基づいて、図９に示すような渋滞区間における走行パターンを生成する。
【０１０５】
なお、高速道路における渋滞の場合には、停止せずに低速で走行すると考えられるので、図９に示す走行パターンを一定速度（例えば０．５Ｖｊ）上へシフトして用いるようにしてもよい。このとき、式１０に示すｌｊと停止回数（ｎ［回］）は以下のようになる。
【０１０６】
【数１０】
ｌｊ＝（１．５Ｖｊ）^２ （１／Ａｃｃ＋１／Ｄｅｃ）／２ …式１２
ｎ＝（Ｌ／２）／ｌｊ
【０１０７】
また、渋滞区間における最初の一定時間の実測走行パターンと予測走行パターンとを比較して、誤差が大きい（例えば、相互相関係数が０．５以下）ときには、実測走行パターンを渋滞区間の終端まで繰り返すように予測を修正してもよい。
【０１０８】
本実施形態の制御も図７に示す制御形態と同様に、ナビゲーション装置または自動変速機やエンジンの制御ユニットのメインプログラムから一定時間間隔で呼び出される。ステップＳ２１では、ＶＩＣＳ受信機にて目的地までの経路上の交通情報を受信する。ステップＳ２２では、渋滞の新たな発生や解消を検出するために、ステップＳ２１において受信した交通情報とその前に受信した情報の差異を調べる。
【０１０９】
ステップＳ２３では、Ｓ２１において受信した交通情報に変化がある場合に交通状況の変化を考慮するために、図７に示す走行パターンの予測を再び行う。
【０１１０】
したがって、ＶＩＣＳ受信機で目的地までの経路における交通情報を受信し、交通状況が変化した場合には、走行パターンの予測を再度行うので、目的地へ到達する間の交通情報の変化に対応した走行パターンの予測ができる。
【０１１１】
第３実施形態
図１０は、本発明の第３実施形態に係る走行パターン生成装置１０の構成を示すブロック図である。
これについて説明すると、測位センサ１０１は衛星からの電波を受信するＧＰＳアンテナ、車両の進行方向を測定するジャイロコンパスで構成される。
【０１１２】
交通情報検出手段１０２は、ＦＭ多重放送や電波ビーコン、光ビーコンなどのインフラからの情報の受信機を備え、道路工事や混雑度といった交通情報をリアルタイムで収集する。
【０１１３】
速度検出手段１０３は、磁気センサや光学式センサで構成され、車輪やドライブシャフトの回転速度から車両の走行速度またはエンジンの回転速度を検出する。
【０１１４】
自車位置検出手段１０４は、ＧＰＳアンテナから得られた緯度、経度、周辺の地図データ上の道路形状と、ジャイロコンパスと速度検出手段から自車両の走行軌跡を比較して道路地図上の自車位置を特定する。
【０１１５】
目的地入力手段１０５は、ドライバが地図の表示画面をスクロールさせて目的地を地図データ上に設定する。
【０１１６】
経路探索手段１０６は、自車位置検出手段１０４で特定した自車位置から目的地入力手段１０５で設定した目的地までの経路を地図データから検索する。経路の探索条件としては、最短距離や高速道路の優先使用などが考えられる。
【０１１７】
経路区分手段１０７は、経路探索手段１０６で検索した目的地までの経路を道路種別や右左折する交差点、料金所などでいくつかの区間に区分する。
【０１１８】
道路情報検出手段１０８は、経路区分手段１０７で区分した目的地までの各区間について、地図データから信号間隔や道路種別、交通情報を検出する。
【０１１９】
加減速周期予測手段１０９および加減速振幅予測手段１１０は、道路情報検出手段１０８で検出した信号間隔や道路種別、交通情報に応じて走行中の加減速の周期および振幅を予測する。
【０１２０】
停止位置予測手段１１１は、道路情報検出手段１０８で検出した信号や交差点、料金所の位置から停止位置を予測する。
【０１２１】
走行速度・加減速予測手段１１２は、道路情報検出手段１０８で検出した道路種別や曲率から走行速度と発進時の加速度、停止時の減速度を予測する。
【０１２２】
走行パターン生成手段１１３は、加減速周期予測手段１０９および加減速振幅予測手段１１０、停止位置予測手段１１１、走行速度・加減速予測手段１１２の予測結果を用いて目的地までの走行パターンを予測する。
【０１２３】
なお、こうした各ブロックの機能は、図１０に示すように、速度検出手段１０３以外をナビゲーション装置内にまとめてもよいし、また加減速周期予測手段１０９および加減速振幅予測手段１１０、走行パターン生成手段１１３等は独立した演算ユニットとしてもよい。
【０１２４】
このような構成を実現するハードウェアの一例を図１１に示す。この例では、ＧＰＳアンテナ、ジャイロコンパスなどで構成される測位センサと、道路勾配や道路種別、交差点の位置などを記録した地図データ、ＶＩＣＳなどのインフラからの交通情報を受信するための受信機で構成されるナビゲーション装置と、アクセル開度、スロットル開度、車速、エンジン回転数、モータ回転数、ＳＯＣ（バッテリの充電率、State of Charge）の各センサ、ブレーキスイッチからの入力信号によってエンジン、モータの駆動力やバッテリのＳＯＣを制御するハイブリッド制御ユニットで構成されている。ここで、本発明における地図データの記録装置には、走行履歴を記録・更新するために、記録可能なＤＶＤドライブやフラッシュメモリなどが用いられる。
【０１２５】
次に、制御手順を説明する。
図１２は本例の基本的な制御手順を示すフローチャートであり、目的地までの経路に関する道路情報を用いて走行パターンを予測する手順の概要を示す。
【０１２６】
まずステップ１１では、目的地入力手段１０５によってドライバが目的地を入力する。なお、ナビゲーション装置が走行経路の履歴を記録しておき、過去の履歴と同様の時間帯に同様の経路を走行している場合は、運転者が目的地を設定しない場合でも、通勤路などの普段よく通る経路を走行すると予測して、自動的に目的地を設定するようにしてもよい。
【０１２７】
ステップ１２では、経路探索手段１０６によってステップ１１で設定した目的地までの経路を探索する。経路探索の条件は有料道路の使用や渋滞区間の回避などを、ドライバが選択できるようにしてもよい。
【０１２８】
ステップ１３では、経路区分手段１０７によってステップ１２で探索した経路の道路情報や交通情報を交通情報検出手段１０８や地図データから抽出し、道路種別や右左折する交差点、料金所、勾配の変化点、道路の混雑状況によって幾つかの区間に区分する。
【０１２９】
次のステップ１４からステップ１８では、ステップ１３で区分した各区間における走行パターンを、現在地から目的地へ向かって順に生成する。
【０１３０】
まずステップ１４では、停止位置予測手段１１１によって交差点や料金所の位置情報から停止位置（Ｓ１〜Ｓｍ）を予測する。停止位置の予測については、上述した第１実施形態と同様に、交差点の平均間隔から直進する交差点での停止回数を予測する手法を採用することができる。
【０１３１】
すなわち、交差点や信号機の平均間隔（Ｘ）より、上記式１のような単調減少関数を用いて停止回数の予測値（ｎ）を求める。一つの区間内で停止する信号機または交差点の選択方法については、まず、直進する交差点については、１２車線道路との交差点、２１車線道路との交差点といった優先順位に従って停止場所を選択する。また、料金所、右折（左側通行の場合）、より優先度の高い道路への右左折（細街路→１車線道路、１車線道路→２車線道路）の場合は、予測停止回数にかかわらず必ず停止すると予測する。
【０１３２】
既述した図２に示す経路を例に挙げて、停止する交差点の選択について説明すると、表１より出発地から交差点Ａまでの区間の予測停止回数は２．６であり、小数点以下を切り上げて３回停止すると予測する。そして、この停止場所として、１車線道路から２車線道路へ左折する交差点Ａ、２車線道路との交差点であるＡ１、および１車線道路との交差点であるＡ２を選択する。
【０１３３】
同様に、交差点ＡからＢまでの区間での予測停止回数は表１より１．１であり、小数点以下を切り捨てて１回停止すると予測する。しかし、この区間には「必ず停止する」と予測することとした料金所Ｂ２と右折する交差点Ｂがあるので、予測停止回数は１回であるが、料金所Ｂ２と交差点Ｂの２個所で停止すると予測する。
【０１３４】
また、過去に走行したことがある区間の場合には、そのときの走行履歴を用いて、停止確率（停止回数／通った回数）の高い交差点または信号機から順に停止場所を選択するように予測しても良い。
【０１３５】
図１２に戻り、ステップ１５では、走行速度・加減速予測手段１１２によって図１３に示すような道路種別と走行速度、加速度、減速度の関係を記録したテーブルを用いて、ステップ１３で収集した道路種別情報から走行速度（Ｖｍ［ｋｍ／ｈ］）、加速度（Ａｍ［ｍ／ｓ^２］）、減速度（Ｄｍ［ｍ／ｓ^２］）を予測する。なお、図１３に示すテーブルの値は、この演算実行時点までの走行における走行速度、加速度、減速度を道路種別毎に平均して記録するようにしてもよい。加速、減速、走行の区別については、加速度が一定値（例えば０．５［ｍ／ｓ^２］）以上の時は加速中、一定値（例えば−０．５［ｍ／ｓ^２］）以下の時は減速中、それ以外のときは走行中とする方法が挙げられる。
【０１３６】
次のステップ１６では、走行パターン生成手段１１３において、加減速周期予測手段１０９に記録された走行中の加減速の周期（Ｔｍ［ｓｅｃ］）を読み出す。
【０１３７】
ステップ１７では、走行パターン生成手段１１３において、加減速振幅予測手段１１０に記録された走行中の加減速における速度の振幅（Ｗｍ［ｋｍ／ｈ］）を定める。なお、走行中の加減速の周期Ｔｍおよび速度の振幅Ｗｍの記録値は、高速道路や一般道などの複数の道路種別を含む経路の実走行データから走行中の加減速の周期および速度の振幅を抽出し、それらの平均値を求めて設定する。
【０１３８】
ステップ１８では、ステップ１４からステップ１７で予測した停止位置、走行速度、加速度、減速度と走行中の加減速の周期および速度の振幅に基づいて走行パターンを生成する。走行パターンは、図１４に示すように、一定の時間間隔で予測速度Ｖ（ｎ）を定めてもよく、また地図データに記録されている道路の座標を表すノードや補完点毎に予測速度Ｖ（ｎ）を定めてもよい。図１４の例では、停止位置Ｓ１から走行速度Ｖｍに到達するまでは加速度Ａｍで加速し、走行時は周期Ｔｍ、振幅の中心がＶｍ、振幅がＷｍの周期関数を用いて一定時間間隔で走行パターンを生成する。図１４のように周期関数に正弦波を用いると、加速度の変化が連続な走行パターンが生成できる。また、三角波を用いると、走行パターン生成の際の演算負荷を軽減することができる。
【０１３９】
減速は次の停止位置Ｓ２までの残りの距離が、現在の速度Ｖ（ｉ）から加速度Ｄｍで停止するのに必要な距離より小さくなった時、すなわち以下の式を満たした時点から速度が０［ｋｍ／ｈ］になるまで加速度Ｄｍで減速する。
【０１４０】
【数１１】
Ｌ−∫Ｖ（ｎ）ｄｔ≦Ｖ（ｉ）^２／（２｜Ｄｍ｜）…式１３
【０１４１】
ここで、Ｌ［ｍ］は停止位置Ｓ１からＳ２までの距離、∫Ｖ（ｎ）［ｍ］はＳ１からの走行距離である。
【０１４２】
ステップ１９では、走行パターンを予測する対象区間が目的地までの経路における最終区間ならば本制御を終了し、そうでなければステップ１４へ戻り次の区間の走行パターン予測を行う。
【０１４３】
以上が本例の基本的な制御手順であるが、このうちステップ１６およびステップ１７の制御内容を以下のように変更することもできる。
【０１４４】
まずステップ１６にて、加減速周期予測手段１０９によってステップ１３で区分した区間の長さを区間内の信号数で除算した信号間隔（Ｓｉｍ［ｍ］）に応じて、走行中の加減速の周期Ｔｍを補正する。また、ステップ１７にて、加減速振幅予測手段１１０によって、ステップ１６と同様に信号間隔Ｓｉｍに応じて走行中の加減速における速度の振幅Ｗｍを補正する。こうした周期Ｔｍおよび振幅Ｗｍの補正値は、例えば図１５に示すような関係式から求める方法が挙げられる。なお、図１５の関係式は、例えば信号間隔が短いほど走行中の加減速の周期は短く、速度の振幅は小さくなるなどの、実走行データを解析した結果得られる傾向を反映させることが望ましい。
【０１４５】
他の改変例として、ステップ１６にて、加減速周期予測手段１０９によって、ステップ１３で区分した区間毎の道路種別に応じて走行中の加減速の周期Ｔｍを補正する。また、ステップ１７にて、加減速振幅予測手段１１０によって、ステップ１６と同様に道路種別に応じて走行中の加減速における速度の振幅Ｗｍを補正する。こうした走行中の加減速の周期および速度の振幅の補正値は、高速道略や一般道（１車線）、一般道（２車線）といった道路種別毎の実走行データから走行中の加減速の周期および速度の振幅を抽出し、それらの平均値を求めて設定することが望ましい。
【０１４６】
さらに他の改変例として、ステップ１６にて、加減速周期予測手段１０９によって、交通情報検出手段１０２によりＶＩＣＳなどのインフラから得られる、ステップ１３で区分した区間毎の混雑度情報（平均走行速度：ＴＤｍ［ｋｍ／ｈ］）に応じて走行中の加減速の周期Ｔｍを補正する。また、ステップ１７にて、加減速振幅予測手段１１０によって、ステップ１６と同様に混雑度（平均走行速度：ＴＤｍ）に応じて走行中の加減速における速度の振幅Ｗｍを補正する。こうした周期Ｔｍおよび振幅Ｗｍの補正値は、例えば図１６に示すような関係式から求める方法が挙げられる。なお、図１６の関係式は、例えば混雑度が高いほど走行中の加減速の周期は短く、速度の振幅は大きくなるなどの、実走行データを解析した結果得られる傾向を反映させることが望ましい。
【０１４７】
このように、加減速の周期と振幅とを、信号間隔、道路種別あるいは混雑度の応じて適宜補正することで、得られる走行パターンをより実走行に近づけることができる。
【０１４８】
第４実施形態
次に本発明の第４実施形態について説明する。この第４実施形態の制御内容は上述した第３実施形態と基本的には同じであるが、走行中の走行パターンを図１２のステップ１３で区分した区間毎に記録して走行中の加減速の周期および振幅を演算し、その区間の道路種別と信号間隔に応じて学習することで、走行中の加減速の周期および振幅の予測値を逐次補正するものである。
【０１４９】
図１７は本例の制御手順を示すフローチャートであり、まずステップ２１では、走行速度Ｖ（ｉ）を記録する。走行速度の記録は、例えば図１８に示すように一定の時間間隔で行ってもよく、または地図データに記録されている道路の座標を表すノードや補完点毎に行ってもよい。
【０１５０】
ステップ２２では、走行位置が区間の終端であればステップ２３へ進み、これまで走行していた区間の走行パターンから走行中の加減速の周期と振幅の演算を開始し、そうでなければステップ２１へ戻って走行速度の記録を続ける。
【０１５１】
ステップ２３では、ステップ２１で記録した、これまで走行していた区間の走行パターンについて走行速度の差分Ｖ（ｉ）−Ｖ（ｉ−１）を演算する。
【０１５２】
ステップ２４では、図１８に示すように、これまで走行していた区間の走行パターンをステップ２３で演算した差分の符号が連続して同じ区間、すなわち加速または減速継続区間に分割する。
【０１５３】
ステップ２５では、ステップ２４で分割した加速または減速継続区間のうち、速度が一定値Ｖｓ（例えば、１５［ｋｍ／ｈ］）以下となる部分があるものを除去することで、停止からの発進加速と停止への減速を除く。図１８の例では、Ｖ（１）からＶ（２）への区間と、Ｖ（８）からＶ（１０）への区間が除かれる対象となる。
【０１５４】
ステップ２６では、ステップ２５で除かれた部分以外の連続している部分の周期を計算する。周期の計算は「加速→減速」または「減速→加速」を繰り返す時間を求め、周期Ｔｍとする方法が挙げられる。なお、区間内に加減速の繰り返しが２周期以上ある場合は、それらの平均値を取る。図１８の例ではＴ（４）−Ｔ（２）とＴ（８）−Ｔ（４）の平均を走行中の加減速の周期とする。また、各々の加速または減速継続区間の長さを２倍した値を平均して周期Ｔｍとする方法も採用できる。図１０の例では以下の式のようになる。
【０１５５】
【数１２】
Ｔｍ＝２×｛（Ｔ（３）−Ｔ（２））＋（Ｔ（４）−Ｔ（３））
＋（Ｔ（６）−Ｔ（４））＋（Ｔ（８）−Ｔ（６））｝／４
【０１５６】
ステップ２７では、ステップ２５で除かれた部分以外の各加速または減速継続区間の速度の変化量の絶対値を求め、その平均値を振幅Ｖｍとする。図１８の例では、振幅は以下の式のようになる。
【０１５７】
【数１３】
Ｗｍ＝（｜Ｖ（２）−Ｖ（３）｜＋｜Ｖ（３）−Ｖ（４）｜
＋｜Ｖ（４）−Ｖ（６）｜＋｜Ｖ（６）−Ｖ（８）｜）／４
【０１５８】
ステップ２８では、ステップ２６およびステップ２７で求めた周期Ｔｍと振幅Ｗｍを用いてこれまでの予測値を補正する。このようにすることで、ドライバの運転傾向に応じた走行中の加減速の周期と振幅を予測することができる。補正は例えば下記のように、これまでの予測値ＴＭ（ｎ）およびＷＭ（ｎ）（ｎは道路種別毎のこれまでの補正回数）との加重平均を用いる方法が挙げられる。
【０１５９】
【数１４】
ＴＭ（ｎ＋１）＝（ＴＭ（ｎ）＋Ｔｍ）／２
ＷＭ（ｎ＋１）＝（ＷＭ（ｎ）＋Ｗｍ）／２
【０１６０】
あるいは、道路種別毎のこれまでの補正回数ｎを記録しておき、下記のように補正回数ｎを用いて加重平均を取る方法もある。
【０１６１】
【数１５】
ＴＭ（ｎ＋１）＝（ｎ×ＴＭ（ｎ）＋Ｔｍ）／（ｎ＋１）
ＷＭ（ｎ＋１）＝（ｍ×ＷＭ（ｎ）＋Ｗｍ）／（ｎ＋１）
【０１６２】
あるいは、下記のように過去一定回数前までの予測値を記録しておき、それらとの平均値を用いる方法もある。
【０１６３】
【数１６】
ＴＭ（ｎ＋１）
＝｛ＴＭ（ｎ−ｐ）＋…＋Ｔｍ（ｎ−１）＋Ｔｍ（ｎ）｝／（ｐ＋１）
ＷＭ（ｎ＋１）
＝｛ＷＭ（ｎ−ｐ）＋…＋Ｗｍ（ｎ−１）＋Ｗｍ（ｎ）｝／（ｐ＋１）
【０１６４】
なお、走行中の加減速の周期と振幅の予測値は道路種別毎に記録しておく。図１８の例では一般道（２車線）についての予測値を補正する。
【０１６５】
ステップ２９では、走行中の加減速の周期と振幅の予測値補正の対象区間が目的までの経路における最終区間ならば本制御を終了し、そうでなければステップ１４へ戻って次の区間の補正を行う。
【０１６６】
第５実施形態
図１９乃至図２１は、本発明のさらに他の実施形態を示すものであり、この第５実施形態の制御内容は上述した第３実施形態と基本的には同じであるが、走行パターン予測を行う区間にカーブがある場合は走行中の加減速の振幅を０に補正する。なおこの他にも、カーブ区間内を走行する時の加減速の周期は長く、振幅は小さく補正する方法を採用しても良い。本制御内容は図１２のステップ１８から呼び出されるサブルーチンとなっている。
【０１６７】
まずステップ２１１では、走行パターン予測を行う区間内の点Ｉにおける曲率ｒ（ｉ）が設定値α（例えば、１／５０）以下であれば、カーブ区間とみなしてステップ２１２へ進む。そうでない場合はステップ２１５へ進み、図２０に示すように、図４のステップ１８の処理と同様に走行時の走行パターンに加減速を付加する。
【０１６８】
ステップ２１２では、曲率ｒ（ｉ）よりカーブ通過速度Ｖｃを求める。曲率とカーブ通過速度との関係式は、例えば図２１に示すような単調減少関数を用いることができる。
【０１６９】
ステップ２１３では、図２０に示すように、走行パターン予測を行う区間内の点Ｉにおける速度Ｖ（ｉ）をステップ２１２で求めたＶｃとする。このようにすることで、図２０のようにカーブ進入後から脱出前までは一定速度Ｖｃで走行するような走行パターンが生成される。
【０１７０】
ステップ２１４では、走行位置が区間の終端であれば本制御を終了し、そうでなければステップ２１１へ戻って走行パターンの生成を続ける。
【０１７１】
以上、第３実施形態乃至第５実施形態の走行パターン生成装置では、走行中の加減速の周期と振幅と振幅の中心とに基づいた周期関数で走行パターンを生成するので、走行中の加減速を含めた目的地までの走行パターンを精度良く予測できる。その結果、従来の走行パターン生成装置では、図２２（ａ）に示すように予測パターンが実走行パターンとの一致性が悪く、燃料消費量の予測信頼性に欠けていたが、本例の走行パターン生成装置では、同図（ｂ）に示すように予測パターンと実走行パターンとが良く一致するので、燃料消費量等の予測信頼性が著しく向上する。
【０１７２】
他の実施形態
ちなみに、本発明の実施形態で求めた目的地までの経路における走行パターンの利用例としては、図２３に示すＨＥＶ（ハイブリッド車）や図２４に示すＦＣＶ（燃料電池電気自動車）に備えられているバッテリの充放電スケジュールの決定が挙げられる。
【０１７３】
以下、上記の手順で予測した走行パターンを用いて、目的地までの経路上におけるエネルギーの充放電スケジュールの決定方法の一例を説明する。
【０１７４】
本例の充放電スケジュールの決定方法の概要は、図２５に示すように以下の処理にしたがって実行される。
【０１７５】
まず、予測した走行パターンを用いて、目的地までの経路における走行に必要な出力を演算する。そして、走行に必要な出力が負になる、すなわちエネルギー回生ができる区間を境界としていくつかのスケジューリング区間に区切る。
【０１７６】
また、次のスケジューリング区間における回生可能なエネルギーを最大限に充電できるように、スケジューリング区間終端でのバッテリ充電量ＳＯＣの値を調節する。そのために該当スケジューリング区間内でのモータ走行などを考える。
【０１７７】
また、ＳＯＣｍａｘ−ＳＯＣｐを出力に換算した値Ｐ（SOCmax−SOCp）（ｗ）が、次のスケジューリング区間で回生可能なエネルギーＣ（ｖ）より小さい場合は、エンジンの作動効率の低い区間からモータ走行を行う。（但し、SOCmaxはバッテリ充電量の上限値、SOCpはスケジューリング区間先頭でのバッテリ充電量である。）
さらに、ＳＯＣｍａｘ−ＳＯＣｐを出力に換算した値Ｐ（SOCmax−SOCp）（ｗ）が、次のスケジューリング区間で回生可能なエネルギーＣ（ｗ）より大きいときは、エンジンのみで走行した場合とモータ走行のために事前に発電を行った場合の燃料消費量を比較して、少ない方を行うよう充放電スケジュールを決定する。
【０１７８】
以上のような基本的な考えに基づき、図２６のフローチャートに従って充放電スケジュール決定の流れを説明する。
【０１７９】
まず、ステップＳａ１では、目的地までの経路上に、ある一定時間（例えば１分）以上継続的にエネルギー回生できる区間があるかどうかを検索し、ある場合はステップＳａ２へ進み、ない場合はステップＳａ６へジャンプする。例えば、図２５に示す経路では、高速道路の出口料金所の手前での減速から一般道の降坂が、この区間に該当する。
【０１８０】
ステップＳａ２では、ステップＳａ１で検索した継続的にエネルギー回生できる区間によって目的地までの経路をスケジューリング区間に区切る。図２５の例では、高速道路の出口料金所の手前での減速を境に２つのスケジューリング区間に区切る。なお、ステップＳａ３以降の処理は、出発地側のスケジューリング区間から順に行う。
【０１８１】
ステップＳａ３では、スケジューリング区間の先頭でのバッテリ充電量SOCpが下限値SOCminの近傍である場合は、ステップＳａ６にジャンプし、そうでない場合はステップＳａ４へ進む。
【０１８２】
ステップＳａ４では、スケジューリング区間の先頭でのバッテリ充電量SOCpがエネルギー回生の途中で上限値SOCmaxを越えてしまう場合、すなわちＳＯＣｍａｘ−ＳＯＣｐを出力に換算した値Ｐ（ＳＯＣｍａｘ−ＳＯＣｐ）（ｖ）が回生できるエネルギーＣ（ｖ）より小さい場合はステップＳａ５へ進み、そうでない場合はステップＳａ６へジャンプする。図２５の例では、バッテリ充電量がスケジューリング区間１の先頭における値SOCpであれば、スケジューリング区間２における回生可能なエネルギーＣ（ｗ）を全て充電できるのでステップＳａ６へ進む。
【０１８３】
ステップＳａ５では、エンジンの運転点が最良効率線から最も離れた区間をモータ走行またはアシストすることによってバッテリを放電するように充放電スケジュールを決定する。図２５の例では、市街地の渋滞の区間が該当する。
【０１８４】
ステップＳａ６では、エンジンの運転点が最良効率線から離れている、すなわちエンジンの作動効率が低い区間がある場合はステップＳａ７へ進み、そうでない場合はステップＳａ８へジャンプする。図２５の例では、市街地の渋滞区間が該当する。
【０１８５】
ステップＳａ７では、ステップＳａ６で検出したエンジンの作動効率が低い区間をエンジンのみで走行、またはバッテリ充電量SOCの値がモータ走行可能であればその分を差し引いてエンジン走行した場合の燃料消費量Ｂと、前記の区間をモータ走行するエネルギーをバッテリに充電するために事前に発電する場合の燃料消費量Ａを比較する。そして、燃料消費量ＡがＢよりも少ない場合は事前に発電する。図２５の例では、スケジューリング区間１の市街地の渋滞をエンジンのみで走行した場合の燃料消費量Ｂよりも、前の一般道で市街地の渋滞をモータ走行するエネルギーを発電した場合の燃料消費量Ａの方が少ないので、一般道において発電して、市街地の渋滞でのモータ走行によりバッテリを消費するよう充放電スケジュールを決定する。なお、発電量は信号機での停止時にエネルギー回生できるように設定する。
【０１８６】
ステップＳａ８では、次に処理するスケジューリング区間が最終区間であればステップＳａ９へ進み、そうでなければステップＳａ３へ戻る。
【０１８７】
ステップＳａ９では、エンジンの運転点が最良効率線から最も離れた区間でモータ走行またはアシストしてバッテリを消費するように充放電スケジュールを決定する。図２５の例では、最終のスケジューリング区間にはエネルギー回生した後には一般道の登坂しかないので、この区間においてモータアシストすることによってバッテリを消費する。
【０１８８】
以上がバッテリの充放電スケジュールの決定方法の一例である。
【０１８９】
このように、目的地までの経路における停止・発進を考慮した走行パターンの予測結果を用いることにより、目的地までの経路におけるエネルギー消費量を厳密に計算することができる。
【０１９０】
また、目的地までの経路における停止・発進を考慮した走行パターンの予測結果を用いることにより、目的地までの経路におけるエネルギーの蓄積および放出スケジュールを厳密に決定することができる。
【０１９１】
本発明の第３実施形態乃至第５実施形態に係る走行パターン生成装置で求められた走行パターンも、上述したエネルギーの充放電スケジュールの決定方法に適用できる。すなわち、走行負荷からエンジンの作動効率を求め、作動効率の高い区間でバッテリを充電して、作動効率の低い区間はモータで走行するようにバッテリ充放電スケジュールを生成する。
【０１９２】
ＨＥＶのバッテリ充放電スケジューリングの概要について説明すると、図２７に示すように、まず図１２のフローチャートの説明で述べたような手法で、目的地までの走行速度パターンを生成する。次に、目的地までの経路の道路勾配と走行速度パターンを用いて、勾配抵抗と走行抵抗、加速抵抗を合わせた目的地までの走行負荷パターンを求める。そして、目的地までの走行負荷パターンと目的地における目標ＳＯＣ（バッテリ充電率）を満たしながら、燃料消費量を低減するようにエンジン／モータの駆動力配分を求める。最後に、その駆動力配分から目的地までのＳＯＣパターンが上限値と下限値の範囲内に収まるように求め、充放電スケジュールを決定する。図２７の例では、市街地における低速走行では走行負荷が小さくエンジンの作動効率が低いためモータ走行を優先し、走行負荷が負となる面積が広い信号や交差点手前での減速や降坂区間ではエネルギーを回生し、降坂の後の登坂では回生で充電したエネルギーを用いてモータでエンジンのアシストを行うように充放電スケジュールを決定する。このように走行中の加減速を考慮して生成した走行パターンを用いることで、走行中の減速による回生量や加速に必要なトルクを正確に予測できるので、ハイブリッド車のバッテリ充放電をより効率よく行うことができ、燃費が向上する。なお、本適用例はＨＥＶだけでなくＥＶ（電気自動車）やＦＣＶにも適用することができ、その場合は信号や交差点手前での減速や降坂区間などでのエネルギー回生が可能な区間の手前ではバッテリの放電を行い、登坂などの大きなモータトルクが必要な区間の手前ではバッテリを充電するようにする。
【０１９３】
本発明の走行パターン生成装置で生成された走行パターンは、エンジンの作動効率マップと燃料消費率マップを用いることで、目的地までに消費する燃料の総量予測にも利用できる。
【０１９４】
図２８は目的地までに消費する燃料の総量を予測する手順を示すフローチャートである。
【０１９５】
まず、ステップ３１１では、速度パターンの各サンプル値Ｖ（ｉ）から走行負荷Ｐ（ｉ）［ｗ］を求める。走行負荷の演算は下記のような関係式を用いる。
【０１９６】
【数１７】
Ｐ（ｉ）＝｛Ｒｒ＋Ｒａ（ｉ）＋Ｒｃ（ｉ）＋Ｒｇ（ｉ）｝×Ｖ（ｉ）
但し、Ｒｒ＝μＭｇ、Ｒａ＝ａｃｃ（ｉ）Ｍ、
Ｒｃ＝０．５ρＣｄＡＶ（ｉ）^２
Ｒｇ＝Ｍｇｓｉｎ｛ａｔａｎ（Ｇｒ（ｉ）／１００）｝
【０１９７】
ここで、Ｍ（ｋｇ）は車重、μは転がり抵抗係数、ρ（ｋｇ／ｍ^３）は空気密度、Ｃｄは空気抵抗係数、Ａ（ｍ^２）は前影投影面積、ｇ（ｍ／ｓ^２）は重力加速度、ａｃｃ（ｍ／ｓ^２）は車両の加速度、Ｇｒ（ｉ）（％）は道路勾配の計算値である。
【０１９８】
ステップ３１２では、図２９に示すようにステップ３１１で求めた走行負荷Ｐ（ｉ）の仕事に対するハイブリッド制御ユニット内で関係付けられた動作点を読出し、エンジンの燃料消費率マップから、このときの燃料消費率ｆ（ｉ）［ｃｃ／ｓｅｃ］を求める。
【０１９９】
ステップ３１３では、サンプル値Ｖ（ｉ）が目的地までの経路における走行パターンの最終サンプルであれば、速度の各サンプル値に対する燃料消費率の演算は目的地まで終了したと判定してステップ３１４へ進み、そうでない場合はステップ３１１へ戻る。
【０２００】
ステップ３１４では、下記のようにステップ３１２で求めた燃料消費率ｆ（ｉ）を出発地から目的地まで積分して総燃料消費量Ｆ［ｃｃ］を求める。
【０２０１】
【数１８】
Ｆ＝∫Ｖ（ｉ）ｄｔ
【０２０２】
この目的地までの燃料消費量予測の適用例としては、図３０に示すように、ナビゲーション装置で目的地を設定した時に、複数の経路候補について求めた燃料消費量に単位量当たりの燃料代を乗じて、燃料代を提示するシステムが考えられる。本制御はＨＥＶだけでなくガソリン車やディーゼル車の燃料消費量予測にも適用することができる。
【０２０３】
以上、本発明の実施形態について説明したが、本発明はハイブリッド車やＦＣＶのバッテリの充放電スケシュールの決定に利用するだけでなく、畜熱アキュムレータを備えたエアコンの畜熱および放熱スケジューリングに用いたり、停止発進に伴うエネルギー収支を考慮した、より正確な目的地までの燃費計算に適用してもよい。また、本発明は以上の実施形態に限定されるのもでなく、それらのいずれかを組合せて実施してもよい。
【図面の簡単な説明】
【図１】本発明の走行パターン生成装置の第１実施形態を示すブロック図である。
【図２】走行予定経路の一例を示す図である。
【図３】平均信号間隔に対する停止回数の一例を示すグラフである。
【図４】道路の曲率半径に対する走行速度の一例を示すグラフである。
【図５】本発明の走行パターン生成装置により生成された走行パターンの一例を示すパターン図である。
【図６】図５に示す走行パターンを時間に対する走行パターンに変換したグラフである。
【図７】本発明の走行パターン生成装置の制御手順を示すフローチャートである。
【図８】本発明の第２実施形態の制御手順を示すフローチャートである。
【図９】図８に示す実施形態の走行パターンを示すパターン図である。
【図１０】本発明の走行パターン生成装置の第３実施形態乃至第５実施形態を示すブロック図である。
【図１１】図１０に示す実施形態をさらに具体化したブロック図である。
【図１２】本発明の第３実施形態の制御手順を示すフローチャートである。
【図１３】道路種別と走行速度、加減速の関係を示す制御マップである。
【図１４】図１０に示す走行パターン生成装置で生成された走行パターンを示すグラフである。
【図１５】信号間隔と加減速の周期、振幅の関係を示すグラフである。
【図１６】混雑度と加減速の周期、振幅の関係を示すグラフである。
【図１７】本発明の第４実施形態の制御手順を示すフローチャートである。
【図１８】第４実施形態の制御手順を説明するための走行パターンの例である。
【図１９】本発明の第５実施形態の制御手順を示すフローチャートである。
【図２０】第５実施形態の制御手順を説明するための走行パターンの例である。
【図２１】カーブ通過速度と曲率の関係を示すグラフである。
【図２２】本発明で得られた走行パターンと従来の走行パターンを示す図である。
【図２３】本発明の走行パターン生成装置が適用されるハイブリッド自動車の一例を示すブロック図である。
【図２４】本発明の走行パターン生成装置が適用される燃料電池電気自動車の一例を示すブロック図である。
【図２５】本発明により生成された走行パターンをハイブリッド自動車に適用した一例を示すパターン図である。
【図２６】図２５に示す実施形態の制御手順を示すフローチャートである。
【図２７】本発明により生成された走行パターンをハイブリッド自動車に適用した他の例を示すパターン図である。
【図２８】本発明で得られた走行パターンを用いて燃料消費量を求める手順を示すフローチャートである。
【図２９】エンジン回転数とエンジン出力トルクの関係を示すグラフである。
【図３０】図２８に示す実施形態をナビゲーション装置に適用した一例を示す図である。
【符号の説明】
１…走行パターン生成装置
１１…道路情報取得手段
１３…停止回数予測手段
１４…走行速度予測手段
１５…加減速度予測手段
１６…走行パターン生成手段[0001]
BACKGROUND OF THE INVENTION
The present invention relates to a travel pattern generation device that automatically predicts a travel pattern on a planned travel route using information related to the planned travel route of the host vehicle.
[0002]
[Prior art]
As this type of travel pattern generation device, for example, an accessory drive control device for an internal combustion engine described in JP-A-9-324665 is known. In this method, the route to be traveled is classified by road type such as “highway” and “urban area”, and the travel speed in each section is predicted to determine the energy accumulation and release schedule.
[0003]
[Problems to be solved by the invention]
However, the conventional device does not fully consider the stop and start that have a large impact on the energy balance in the section classified by road type, so the energy balance due to the stop and start necessary for actual travel is omitted. End up.
[0004]
For this reason, there has been a problem that accurate energy storage and discharge scheduling in consideration of regeneration of deceleration energy at the time of stopping and driving force assist for acceleration at the time of starting cannot be performed.
[0005]
The present invention has been made in view of the above-described problems of the prior art, and a traveling pattern generation device capable of generating a more strict energy balance schedule by predicting the number of stops and a stop position in a planned traveling route. The purpose is to provide.
[0006]
[Means for Solving the Problems]
(1) To achieve the above purpose,The present inventionThe travel pattern generation device has road information acquisition means for acquiring information related to the planned travel route of the host vehicle, and generates a travel pattern of the host vehicle on the planned travel route using the information acquired by the road information acquisition means. The travel pattern generation device further includes stop number prediction means for predicting the number of stops of the host vehicle on the planned travel route using the information acquired by the road information acquisition means.
[0007]
Also,The present inventionThe travel pattern generation apparatus further includes travel speed prediction means for predicting the travel speed of the host vehicle on the planned travel route using the information acquired by the road information acquisition means.
[0008]
further,The present inventionThe travel pattern generation apparatus further includes an acceleration / deceleration prediction unit that predicts the acceleration / deceleration of the host vehicle on the planned travel route using the information acquired by the road information acquisition unit.
[0009]
Also,Of the present inventionThe travel pattern generation device is configured to generate a travel pattern of the host vehicle based on the number of stops predicted by the stop number prediction means, the travel speed predicted by the travel speed prediction means, and the acceleration / deceleration predicted by the acceleration / deceleration prediction means. It is further characterized by further comprising travel pattern generation means for generating
[0010]
Of the present inventionThe travel pattern generator uses the road information about the planned travel route to predict not only the speed and acceleration but also the number of stops. Therefore, the stop required for actual travel, especially in vehicles equipped with energy storage means such as hybrid vehicles, etc. In addition, the travel pattern takes into account the energy balance associated with the start, and more accurate energy storage and release scheduling can be performed.
[0011]
(2) Various forms can be adopted for the prediction method in the stop number prediction means of the above invention.
[0012]
The present inventionIn the travel pattern generation apparatus, the stop number predicting means includes first stop number predicting means for detecting the position of an intersection and a traffic light on the planned travel route of the own vehicle acquired by the road information acquiring means, Dividing the planned travel route into several sections, obtaining an average interval between intersections and traffic lights existing in each section, and predicting the number of stops of the host vehicle in each section based on this average interval Means.
[0013]
The present inventionThen, the planned travel route is divided into several sections according to the road type and large intersections, and the number of stops is predicted from the average interval between the intersections and traffic lights existing in the divided sections. Can predict.
[0014]
Also,The present inventionIn the traveling pattern generation apparatus, the second stop number predicting means varies the number of stops according to the traveling time zone.
[0015]
The present inventionIn the actual traffic situation, the predicted number of stops will be increased or decreased according to the time zone during which the vehicle passes the planned travel route, so that the number of stops will be higher during morning and evening rush hours and less during late night and early morning. It is possible to predict the number of stops adapted to
[0016]
  further,Of the present inventionIn the travel pattern generation device, the second stop number prediction means predicts that the vehicle will stop at an intersection where the host vehicle turns in a direction crossing the oncoming lane on the planned travel route.
[0017]
The present inventionTherefore, it is predicted that the vehicle will stop at the intersection where the host vehicle turns in the direction crossing the opposite lane on the planned travel route, for example, at the intersection where the vehicle turns to the right for left-hand traffic and to the left for right-hand traffic. Can predict.
[0018]
  Also,The present inventionIn the travel pattern generation device, the second stop number predicting means predicts that the vehicle stops at an intersection that crosses a road that has a higher priority than the host vehicle or turns right or left on the road that has a higher priority than the host vehicle. To do.
[0019]
  The present inventionIn the planned travel route, it is predicted that the vehicle will turn right or left on a road with higher priority than the road on which it is traveling, or stop at an intersection that crosses a road with higher priority. Can do.
[0020]
  further,The present inventionIn the travel pattern generation device, the second stop number prediction means predicts that the vehicle will stop at an intersection with a road having a higher priority than the host vehicle in the planned travel route.
[0021]
  The present inventionThen, since it is predicted to stop at an intersection with a road having a higher priority among the intersections in the planned travel schedule, it is possible to predict a stop place that is more suitable for actual driving.
[0022]
  Also,The present inventionIn the travel pattern generating apparatus, the second stop number predicting means predicts that the toll station on the planned travel route stops.
[0023]
  The present inventionThen, when there is a toll gate on the planned travel route, it is predicted that the vehicle stops at that location, so that it is possible to predict a stop location that is more suitable for actual driving.
[0024]
  further,The present inventionThe travel pattern generation device further includes a travel history storage unit that stores a past travel history, and the second stop number prediction unit is configured to determine the number of travels based on the past travel history stored in the travel history storage unit. Stop locations are selected in order from the intersection or traffic light with the largest percentage of stops.
[0025]
  BookIn the invention, the stop location is selected in order from the intersection or traffic signal having the highest ratio of the number of stops to the number of passes using the past travel history, so it is possible to predict the stop location more suitable for actual driving. .
[0026]
  Also,The present inventionIn the travel pattern generation device, when the traffic situation on the planned travel route changes, the travel pattern is generated again based on the changed information.
[0027]
  BookIn the present invention, when the traffic situation on the planned travel route changes, the travel pattern prediction is performed again, so that the travel pattern prediction corresponding to the traffic information change while reaching the destination can be performed.
[0028]
(3) To achieve the above objective,The present inventionThe travel pattern generation device has road information acquisition means for acquiring information related to the planned travel route of the host vehicle, and generates a travel pattern of the host vehicle on the planned travel route using the information acquired by the road information acquisition means. In the travel pattern generation device, the route classifying unit that classifies the planned travel route based on the information acquired by the road information acquiring unit, and the travel of the host vehicle based on the road information in the section segmented by the route classifying unit. Travel speed predicting means for predicting speed, acceleration / deceleration cycle predicting means for predicting acceleration / deceleration period during traveling in the section divided by the route classifying means, and acceleration during driving in the section divided by the route classifying means Acceleration / deceleration amplitude prediction means for predicting deceleration amplitude, the period obtained by the acceleration / deceleration period prediction means, and the acceleration / deceleration amplitude prediction means Traveling pattern generating means for generating a speed pattern waveform during traveling using a periodic function based on the amplitude obtained and the center of the amplitude obtained by the traveling speed predicting means, To do.
[0029]
BookIn the present invention, since the traveling pattern is generated by a periodic function based on the acceleration / deceleration period, amplitude, and center of the amplitude during traveling, it is possible to accurately predict the traveling pattern to the destination including acceleration / deceleration during traveling.
[0030]
Also,The present inventionThe travel pattern generation device further includes signal interval calculation means for obtaining a signal interval by dividing the length of the section divided by the route dividing means by the number of signals existing in the section, and the acceleration / deceleration cycle prediction means Corrects the acceleration / deceleration period during traveling according to the signal interval obtained by the signal interval computing means, and the acceleration / deceleration amplitude predicting means is running according to the signal interval obtained by the signal interval computing means. Correct the acceleration / deceleration amplitude.
[0031]
BookIn the invention, the acceleration / deceleration period and the acceleration / deceleration amplitude are corrected according to the signal interval, so that it is possible to generate a travel pattern closer to actual travel.
[0032]
  Also,The present inventionThe travel pattern generation device further includes road type detection means for detecting the road type of each section divided by the route classification means, and the acceleration / deceleration cycle prediction means is the road type obtained by the road type detection means. The acceleration / deceleration period during traveling is corrected according to the acceleration / deceleration period, and the acceleration / deceleration amplitude predicting means corrects the acceleration / deceleration amplitude during traveling according to the road type obtained by the road type detecting means.
[0033]
  BookIn the invention, the acceleration / deceleration period and the acceleration / deceleration amplitude are corrected according to the road type, so that it is possible to generate a travel pattern closer to actual travel.
[0034]
  Also,The present inventionThe travel pattern generation device further includes a congestion degree detection unit that detects a congestion degree of each of the sections divided by the route classification unit, and the acceleration / deceleration period prediction unit includes the congestion degree obtained by the congestion degree detection unit. The acceleration / deceleration period during traveling is corrected according to the acceleration / deceleration period, and the acceleration / deceleration amplitude predicting means corrects the acceleration / deceleration amplitude during traveling according to the congestion degree obtained by the congestion degree detecting means.
[0035]
  BookIn the invention, the acceleration / deceleration period and the acceleration / deceleration amplitude are corrected according to the degree of congestion, so that it is possible to generate a travel pattern closer to actual travel.
[0036]
Also,The present inventionThe travel pattern generation device of the present invention is divided by a signal interval calculation unit that obtains a signal interval by dividing the length of the section divided by the route dividing unit by the number of signals present in the section, and the route dividing unit. The vehicle further comprises road type detection means for detecting the road type of each section and travel speed detection means for detecting the travel speed of the host vehicle, wherein the acceleration / deceleration cycle prediction means is the travel speed obtained by the travel speed detection means. And the acceleration / deceleration period excluding start acceleration from stop and deceleration until stop according to the signal interval obtained by the signal interval calculation means and the road type obtained by the road type detection means. The predicted value is corrected by learning, and the acceleration / deceleration amplitude prediction means uses the travel speed obtained by the travel speed detection means, and uses the travel speed obtained by the travel speed detection means to detect the signal interval and the road type detection. Depending on the road type obtained by means corrects the estimated value by learning the amplitude of the acceleration and deceleration excluding the start acceleration and the deceleration to a stop from the stop.
[0037]
  BookIn the invention, the driving acceleration / deceleration period and acceleration / deceleration amplitude are learned sequentially during driving according to the signal interval and road type, and this learning value is used when predicting the driving pattern. Can be generated.
[0038]
  Also,The present inventionThe travel pattern generation device further includes a curve detection unit that detects a curve of each of the sections divided by the route division unit, and the acceleration / deceleration period prediction unit is running in the curve obtained by the curve detection unit. The acceleration / deceleration period is corrected, and the acceleration / deceleration amplitude prediction means corrects the acceleration / deceleration amplitude during traveling in the curve obtained by the curve detection means.
[0039]
  BookIn the invention, since the acceleration / deceleration period and the acceleration / deceleration amplitude are corrected according to the curve, it is possible to generate a traveling pattern closer to actual traveling.
[0040]
(4)Of the present inventionThe travel pattern generation device further includes energy consumption calculation means for calculating the energy consumption of the host vehicle on the travel route to the destination based on the travel pattern of the host vehicle generated by the travel pattern generation unit.
[0041]
  BookThe invention uses a travel pattern that takes into account the stop and start of the travel route to the destination, so it takes into account the driving force assist for regeneration of deceleration energy at the time of stop and acceleration at the time of start, and strict energy consumption. Can be calculated.
[0042]
  In the present invention, the energy balance for calculating the energy storage and release schedule for the energy storage means of the own vehicle in the travel route to the destination based on the travel pattern of the own vehicle generated by the travel pattern generation means. A schedule calculation means is further provided.
[0043]
  BookThe invention uses a travel pattern that takes into account the stop and start of the travel route to the destination, so it takes into account the regeneration of deceleration energy at the time of stop and driving force assist for acceleration at the time of start, etc. And a release schedule can be determined.
[0044]
【The invention's effect】
  (1)BookAccording to the invention, particularly in a vehicle equipped with energy storage means such as a hybrid vehicle, a travel pattern in which an energy balance accompanying stop and start necessary for actual travel is taken into consideration, and more accurate energy storage and release scheduling is performed. Can do.
[0045]
  In addition to this,BookAccording to the invention, the number of stops according to the characteristics of the road can be predicted.
[0046]
  Also,BookAccording to the present invention, it is possible to predict the number of stops adapted to actual traffic conditions, such as increasing the number of stops during morning and evening rush hours and decreasing the number of stops in the middle of the night or early morning.
[0047]
  furtherBookAccording to the invention, it is possible to predict a stop location that is more suitable for actual driving.
[0048]
  BookAccording to the invention, it is possible to predict a traveling pattern corresponding to a change in traffic information while reaching the destination.
[0049]
  (2)BookAccording to the invention, since the traveling pattern is generated with a periodic function based on the acceleration / deceleration period, amplitude and center of the amplitude during traveling, the traveling pattern to the destination including acceleration / deceleration during traveling is accurately predicted. it can.
[0050]
  In addition to this,BookAccording to the invention, the acceleration / deceleration period and the acceleration / deceleration amplitude are corrected according to the signal interval, the road type, the congestion degree, the curve, or by using the learning function. Can do.
[0051]
  (3)BookAccording to the invention, it is possible to calculate the exact energy consumption in consideration of regeneration of deceleration energy at the time of stopping, driving force assist for acceleration at the time of starting, and the like.
[0052]
  furtherBookAccording to the invention, it is possible to determine a strict energy accumulation and release schedule in consideration of regeneration of deceleration energy at the time of stopping, driving force assist for acceleration at the time of starting, and the like.
[0053]
DETAILED DESCRIPTION OF THE INVENTION
Hereinafter, embodiments of the present invention will be described with reference to the drawings.
First embodiment
As shown in FIG. 1, the travel pattern generation device 1 of this embodiment includes a road information acquisition unit 11, a clock 12, a stop count prediction unit 13, a travel speed prediction unit 14, an acceleration / deceleration prediction unit 15, and a travel pattern generation unit 16. It has.
[0054]
The road information acquisition means 11 of this example is composed of a navigation device, and detects information related to a route on which the host vehicle is scheduled to travel to the destination (hereinafter also referred to as scheduled travel route). This navigation device consists of a GPS antenna, gyro compass, road map data, and VICS (Vehicle Information & Communication System) receiver for receiving traffic information such as traffic jams. Records the road curvature, width, type, intersection, traffic signal location, etc.
[0055]
The timepiece 12 of this example outputs the current time, and is used to estimate the time when the host vehicle passes through the planned travel route.
[0056]
The number-of-stops prediction side means 13 predicts the number of stops by using the road type and intersection of the planned travel route obtained by the road information acquisition means 11, the information on the position of the traffic light, and the time from the clock 12.
[0057]
The travel speed predicting unit 14 predicts the travel speed using information on the road type and curvature of the planned travel route obtained by the road information acquiring unit 11.
[0058]
The acceleration / deceleration prediction unit 15 predicts the acceleration / deceleration of the planned travel route from the data related to the road type of the planned travel route and the past travel history obtained by the road information acquisition unit 11.
[0059]
The travel pattern generation means 16 combines the prediction results of the stop count prediction means 13, the travel speed prediction means 14, and the acceleration / deceleration prediction means 15 to generate a travel pattern on the planned travel route to the destination, for example, a speed pattern.
[0060]
Next, the control contents of this example will be described.
FIG. 7 is a flowchart showing the processing of the travel pattern generation device 1 of this example, which is called at regular time intervals from the main program of the navigation device, automatic transmission, or engine control unit.
[0061]
This will be explained. First, in step S11, a route to the destination is set by the navigation device, and information regarding the road type, curvature, gradient, traffic light, and intersection position of the set route is read from the road map data.
[0062]
Next, in step S12, the route to the destination set in step S11 is divided into several sections, with points where road types change and main intersections as delimiters. For example, in the case of the route shown in FIG. 2, from the departure point to the intersection A that hits a two-lane road, from this intersection A to the intersection B of this lane road, and from this intersection B to the intersection C that makes a right turn to the narrow street And from the intersection C to the intersection D that goes out to the one-lane road and the section from the intersection D to the destination. Then, subsequent speed pattern prediction operations are sequentially performed from the section on the departure side.
[0063]
In step S13, the predicted value (n) of the number of stops is obtained from the average interval (X) of the intersections and traffic lights using, for example, a monotone decreasing function such as the following Equation 1. In addition, Formula 1 can be calculated | required from experimental data as shown in the graph of FIG.
[0064]
[Expression 1]
n = {− 3.85 · ln (x) +23.6} 1/1000 Equation 1
[0065]
Here, n is the predicted number of stops, x is the average traffic signal interval, and l is the section extension [m].
[0066]
Here, when the number of predicted stops in each section obtained by dividing the route shown in FIG. Here, the decimals of the predicted stop count are rounded off to be used. However, depending on the time of the clock 12 shown in FIG. From 17:00, the decimal point may be rounded up by 0.5 or more, and from 20:00 to 7:00, the decimal point may be rounded down. The setting of the time zone here is appropriately changed according to the place and the traveling direction.
[0067]
[Table 1]

[0068]
Next, a method of selecting a traffic signal or an intersection that stops within one section will be described.
First, for an intersection that goes straight ahead, a stop location is selected according to a priority order such as an intersection with a 12-lane road and an intersection with a 21-lane road. In addition, in the case of a toll booth, right turn (for left-hand traffic), and right / left turn to a higher priority road (narrow street → 1 lane road, 1 lane road, 1 lane road, 2 lane road) Expect to stop.
[0069]
Now, taking the route shown in FIG. 2 as an example, the selection of the intersection to be stopped will be described. From Table 1, the number of predicted stops in the section from the departure point to the intersection A is 2.6, and the number after the decimal point is rounded up to 3 Predicted to stop once. And as this stop place, the intersection A which turns left from the 1-lane road to the 2-lane road, A1 which is the intersection with the 2-lane road, and A2 which is the intersection with the 1-lane road are selected.
[0070]
Similarly, the number of predicted stops in the section from intersection A to B is 1.1 from Table 1, and it is predicted that the number of decimal stops will be rounded off once. However, because there is a toll booth B2 and an intersection B that makes a right turn in this section, the number of predicted stops is one, but it stops at two places, the toll booth B2 and the intersection B. I predict that.
[0071]
In the case of a section that has traveled in the past, it is predicted that the stop location will be selected in order from the intersection or traffic signal with the highest stop probability (number of stops / number of passes) using the travel history at that time. May be.
[0072]
In step S14, the traveling speed is predicted using the information collected in step S11. As an example of a method for predicting the traveling speed (Vs [km / h]), a method using the road type and the curvature of the curve as shown in Equation 2 below can be considered. Here, Vcrv (r) is a function for the curvature radius r [m] of the road as shown in FIG. 4, for example.
[0073]
[Expression 2]
Vs = Vcrv (r) + Ts Equation 2
[0074]
However, Ts is -10 when the planned travel route is a narrow street, 0 when it is a one-lane road, 10 when it is a two-lane road, and 45 when it is a highway.
[0075]
In step S15, the acceleration / deceleration is predicted using the information collected in step S11. Here, acceleration (Acc [m / s²]) And deceleration (Dec [m / s²]) Is an example of a prediction method using road types as shown in the following formulas 3 and 4.
[0076]
[Equation 3]
Acc = 0.76 + Ta Equation 3
[0077]
However, Ta is 0 when the planned travel route is a narrow street or a one-lane road, -0.03 when the route is a two-lane road, 0.15 when the route is an expressway, and when the route is a main road -0.03.
[0078]
Dec = −1.11 + Td Equation 4
However, Td is 0 when the planned travel route is a narrow street or a one-lane road, -0.14 when the route is a two-lane road, 0.03 when the route is a highway introduction route, and when the route is a highway main route 0.36.
[0079]
Table 2 shows the results of predicting the traveling speed, acceleration, and deceleration on the route shown in FIG. 2 using Expressions 2 to 4 in Steps S14 and S15.
[0080]
[Table 2]

[0081]
Next, in step S16, a predicted waveform of a travel pattern is generated for each section by combining the number of stops, travel speed, and acceleration / deceleration predicted in steps S13 to S15. FIG. 5 shows travel patterns generated according to the number of stops on the route shown in Table 1 and Table 2 and the predicted results of travel speed, acceleration, and deceleration on the route of FIG.
[0082]
By the way, when such a running pattern is used for vehicle control such as determination of a battery charge / discharge schedule of a hybrid vehicle, it is more convenient to use the time axis as a reference. A conversion method from the distance axis shown in FIG. 5 to the time axis shown in FIG. 6 will be described below.
[0083]
As shown in FIG. 6, the distance traveled during acceleration and deceleration is Sa [m] and Sd [m], the distance traveled during constant speed travel is Ss [m], and the distance traveled from start to stop (distance between intersections) If L is [m], the following equation holds.
[0084]
[Expression 4]
L = Sa + Ss + Sb Equation 5
[0085]
Here, Sa and Sd can be expressed as follows using the traveling speed Vs, the acceleration Acc, and the deceleration Dec.
[0086]
[Equation 5]
Sa = (Vs / 3.6)²/2Acc...Formula 6
Sd = (Vs / 3.6)²/ 2Dec ... Formula 7
[0087]
Further, the time Ts [sec] for traveling at a constant speed can be expressed as follows.
[0088]
[Formula 6]
Ts = Ss / (Vs / 3.6) Equation 8
[0089]
Substituting Equation 6 through Equation 8 into Equation 5 gives Ts as follows.
[0090]
[Expression 7]
Ts = L / (Vs / 3.6)
-(Vs / 3.6) (1 / Acc-1 / Dec) / 2 Formula 9
[0091]
Thus, it is possible to determine the acceleration time, constant speed travel time, and deceleration time from the predicted values of the travel speed, acceleration, and deceleration, and generate a travel pattern based on the time axis.
[0092]
Finally, in step S17, it is determined whether or not the travel pattern prediction for all sections up to the destination has been completed.
[0093]
As described above, according to the travel pattern generation device 1 of the present embodiment, the travel speed, acceleration, deceleration, and number of stops using information on the road type of the route to the destination, the curvature of the curve, the position of the intersection and the traffic light, and the like. Therefore, it is possible to generate a travel pattern in consideration of road conditions by predicting the number of stops using the average distance between intersections and traffic lights.
[0094]
In addition, the number of decimals after the number of predicted stops is rounded off during the day according to the time of day, and the number of rounds after the decimal point is reduced to 4 or less during morning and evening rush hours when traffic is heavy. By increasing the number of rounds after the decimal point to 6 or more at midnight or early morning, the number of stops adapted to traffic conditions can be predicted.
[0095]
In addition, at an intersection that turns right, an intersection that makes a right or left turn to a higher priority road, or a toll gate, it is predicted that the vehicle will always stop at that location, so it is possible to predict the number of stops suitable for actual driving.
[0096]
Furthermore, since it is predicted that the intersection that goes straight ahead will stop at an intersection with a road with higher priority, the number of stops that are more suitable for actual driving can be predicted in this respect as well.
[0097]
Also, in the section that has traveled in the past, the stop location is selected in order from the intersection or traffic signal with the highest stop probability {(number of stops) / (number of passes)) using the past travel history. It is possible to predict the number of stops suitable for operation.
[0098]
Second embodiment
Next, a second embodiment of the present invention will be described.
The control contents of the present embodiment are basically the same as those shown in FIG. 7 except that the generation of a travel pattern for a traffic jam section is added in step S16. Moreover, the process about the re-prediction of the driving | running | working pattern for responding to the change of the traffic condition shown in FIG. 8 is added.
[0099]
Hereinafter, the description of the control process common to the above-described first embodiment will be omitted, and the process performed in step S16 will be described. In step S16, when traffic information is received on the route to the destination by the VICS receiver that lays the navigation device, in the case of a general road, the number of stops in the traffic jam section is predicted as follows: To do.
[0100]
That is, FIG. 9 shows a travel pattern model in a traffic jam. Here, it is assumed that after accelerating until reaching a certain constant speed, the vehicle immediately starts decelerating and stops. If the maximum value of the speed of the travel pattern shown in FIG. 9 is a value obtained by multiplying the average travel speed (Vj [km / h]) of the traffic jam section transmitted from the VICS center by a certain value (for example, 2), The distance (lj [m]) traveled from the start to the stop is as follows.
[0101]
[Equation 8]
lj = 2Vj²(1 / Acc + 1 / Dec) Equation 10
[0102]
Therefore, if the length of the traffic jam section is L [m], the number of stops in the traffic jam section is L / lj [times]. The average time interval Tj [sec] from the stop to the next start is as follows.
[0103]
[Equation 9]
Tj = (L / Vj) / (L / lj) = lj / Vj Equation 11
[0104]
Based on the number of stops determined above and the average time interval Tj from the stop to the next start, a travel pattern in a traffic jam section as shown in FIG. 9 is generated.
[0105]
In the case of a traffic jam on an expressway, it is considered that the vehicle travels at a low speed without stopping. Therefore, the travel pattern shown in FIG. 9 may be shifted to a certain speed (for example, 0.5 Vj) and used. At this time, lj and the number of stops (n [times]) shown in Expression 10 are as follows.
[0106]
[Expression 10]
lj = (1.5Vj)²(1 / Acc + 1 / Dec) / 2 Formula 12
n = (L / 2) / lj
[0107]
In addition, when the measured travel pattern for the first fixed time in the traffic jam section is compared with the predicted travel pattern, and the error is large (for example, the cross-correlation coefficient is 0.5 or less), the actual travel pattern is displayed until the end of the traffic jam section. The prediction may be modified to repeat.
[0108]
Similarly to the control mode shown in FIG. 7, the control according to the present embodiment is also called at regular time intervals from the main program of the navigation device or the automatic transmission or the engine control unit. In step S21, traffic information on the route to the destination is received by the VICS receiver. In step S22, a difference between the traffic information received in step S21 and the previously received information is examined in order to detect a new occurrence or cancellation of a traffic jam.
[0109]
In step S23, when the traffic information received in S21 is changed, the travel pattern shown in FIG.
[0110]
Therefore, when the traffic information on the route to the destination is received by the VICS receiver and the traffic situation changes, the driving pattern is predicted again, so that it corresponds to the change of the traffic information while reaching the destination. A driving pattern can be predicted.
[0111]
Third embodiment
FIG. 10 is a block diagram showing a configuration of a travel pattern generation device 10 according to the third embodiment of the present invention.
To explain this, the positioning sensor 101 includes a GPS antenna that receives radio waves from a satellite and a gyrocompass that measures the traveling direction of the vehicle.
[0112]
The traffic information detection means 102 includes a receiver for information from infrastructure such as FM multiplex broadcasting, radio wave beacons, and optical beacons, and collects traffic information such as road works and congestion in real time.
[0113]
The speed detecting means 103 is composed of a magnetic sensor or an optical sensor, and detects the traveling speed of the vehicle or the rotational speed of the engine from the rotational speeds of the wheels and the drive shaft.
[0114]
The vehicle position detection unit 104 compares the road shape on the latitude, longitude, and surrounding map data obtained from the GPS antenna with the traveling locus of the vehicle from the gyrocompass and the speed detection unit, and compares the vehicle on the road map. Identify the location.
[0115]
In the destination input means 105, the driver scrolls the map display screen and sets the destination on the map data.
[0116]
The route search means 106 searches the map data for a route from the vehicle position specified by the vehicle position detection means 104 to the destination set by the destination input means 105. The route search condition may be the shortest distance or priority use of an expressway.
[0117]
The route classifying means 107 classifies the route to the destination searched by the route searching means 106 into several sections according to the road type, the intersection for turning left and right, the toll gate, and the like.
[0118]
The road information detection means 108 detects the signal interval, road type, and traffic information from the map data for each section to the destination divided by the route classification means 107.
[0119]
The acceleration / deceleration cycle prediction unit 109 and the acceleration / deceleration amplitude prediction unit 110 predict the acceleration / deceleration cycle and amplitude during travel according to the signal interval, road type, and traffic information detected by the road information detection unit 108.
[0120]
The stop position predicting means 111 predicts the stop position from the signal detected by the road information detecting means 108, the intersection, and the toll gate position.
[0121]
The traveling speed / acceleration / deceleration prediction unit 112 predicts the traveling speed, the acceleration at the start, and the deceleration at the stop from the road type and curvature detected by the road information detection unit 108.
[0122]
The travel pattern generation unit 113 predicts the travel pattern to the destination using the prediction results of the acceleration / deceleration period prediction unit 109, the acceleration / deceleration amplitude prediction unit 110, the stop position prediction unit 111, and the travel speed / acceleration / deceleration prediction unit 112. .
[0123]
As shown in FIG. 10, the functions of these blocks may be integrated in the navigation device other than the speed detection unit 103, and the acceleration / deceleration period prediction unit 109, the acceleration / deceleration amplitude prediction unit 110, and the travel pattern generation The means 113 or the like may be an independent arithmetic unit.
[0124]
An example of hardware that realizes such a configuration is shown in FIG. In this example, a positioning sensor composed of a GPS antenna, a gyrocompass, etc., a receiver for receiving traffic data from infrastructure such as VICS, map data recording road gradients, road types, intersection positions, etc. Engine, motor based on input signals from the navigation device, accelerator opening, throttle opening, vehicle speed, engine speed, motor speed, SOC (battery charge rate, State of Charge) sensors, and brake switch And a hybrid control unit for controlling the SOC of the battery. Here, a recordable DVD drive, flash memory, or the like is used in the map data recording apparatus of the present invention in order to record and update the travel history.
[0125]
Next, a control procedure will be described.
FIG. 12 is a flowchart showing a basic control procedure of this example, and shows an outline of a procedure for predicting a travel pattern using road information related to a route to a destination.
[0126]
First, in step 11, the driver inputs a destination by the destination input means 105. Note that if the navigation device records the travel route history and is traveling on the same route in the same time zone as the past history, even if the driver does not set the destination, A destination may be automatically set by predicting that the vehicle normally travels along a route that passes frequently.
[0127]
In step 12, the route search means 106 searches for a route to the destination set in step 11. The route search condition may allow the driver to select use of a toll road or avoiding a traffic jam section.
[0128]
In step 13, the road information and traffic information of the route searched in step 12 by the route classifying means 107 are extracted from the traffic information detecting means 108 and the map data, and the road type, the intersection for turning left and right, the toll gate, the slope changing point, It is divided into several sections according to road congestion.
[0129]
In the next step 14 to step 18, the traveling pattern in each section divided in step 13 is generated in order from the current location to the destination.
[0130]
First, in step 14, the stop position prediction means 111 predicts stop positions (S1 to Sm) from the position information of intersections and tollgates. About the prediction of a stop position, the method of estimating the frequency | count of a stop in the intersection which goes straight from the average space | interval of an intersection can be employ | adopted similarly to 1st Embodiment mentioned above.
[0131]
That is, the predicted value (n) of the number of stops is obtained from the average interval (X) of the intersections and traffic lights using a monotonically decreasing function such as the above-mentioned formula 1. Regarding the method of selecting a traffic light or an intersection that stops within one section, first, for an intersection that goes straight, a stop location is selected according to a priority order such as an intersection with a 12-lane road and an intersection with a 21-lane road. In addition, in the case of a toll booth, right turn (in the case of left-hand traffic), and right / left turn to a higher priority road (narrow street → 1 lane road, 1 lane road → 2 lane road) Expect to stop.
[0132]
Taking the route shown in FIG. 2 as an example, the selection of the intersection to be stopped will be described. From Table 1, the number of predicted stops in the section from the departure point to the intersection A is 2.6, and the number after the decimal point is rounded up. Predicted to stop 3 times. And as this stop place, the intersection A which turns left from the 1-lane road to the 2-lane road, A1 which is the intersection with the 2-lane road, and A2 which is the intersection with the 1-lane road are selected.
[0133]
Similarly, the number of predicted stops in the section from intersection A to B is 1.1 from Table 1, and it is predicted that the number of decimal stops will be rounded off once. However, because there is a toll booth B2 and an intersection B that makes a right turn in this section, the number of predicted stops is one, but it stops at two places, the toll booth B2 and the intersection B. I predict that.
[0134]
In the case of a section that has traveled in the past, it is predicted that the stop location will be selected in order from the intersection or traffic signal with the highest stop probability (number of stops / number of passes) using the travel history at that time. May be.
[0135]
Returning to FIG. 12, in step 15, the roads collected in step 13 using the table in which the relationship between the road type and the traveling speed, acceleration, and deceleration as shown in FIG. 13 is recorded by the traveling speed / acceleration / deceleration prediction unit 112. From the type information, traveling speed (Vm [km / h]), acceleration (Am [m / s]²]), Deceleration (Dm [m / s²]). Note that the values in the table shown in FIG. 13 may be recorded by averaging the traveling speed, acceleration, and deceleration in the traveling up to the point of execution of the calculation for each road type. Regarding the distinction between acceleration, deceleration, and running, the acceleration is a constant value (for example, 0.5 [m / s²]) Or more during acceleration, a constant value (for example, -0.5 [m / s²]) In the following cases, there is a method in which the vehicle is decelerating, and in other cases, the vehicle is traveling.
[0136]
In the next step 16, the traveling pattern generation unit 113 reads the acceleration / deceleration period (Tm [sec]) during traveling recorded in the acceleration / deceleration period predicting unit 109.
[0137]
In step 17, the traveling pattern generation unit 113 determines the speed amplitude (Wm [km / h]) in acceleration / deceleration during traveling recorded in the acceleration / deceleration amplitude prediction unit 110. The recorded values of acceleration / deceleration period Tm and speed amplitude Wm during traveling are the acceleration / deceleration period and speed amplitude during traveling from actual traveling data of a route including a plurality of road types such as highways and ordinary roads. Are extracted and the average value of them is obtained and set.
[0138]
In step 18, a travel pattern is generated based on the stop position, travel speed, acceleration, deceleration, acceleration / deceleration period during travel, and speed amplitude predicted in step 14 to step 17. As shown in FIG. 14, the travel pattern may determine the predicted speed V (n) at regular time intervals, and the predicted speed V for each node or complementary point representing the road coordinates recorded in the map data. (N) may be determined. In the example of FIG. 14, the vehicle is accelerated at an acceleration Am until reaching the travel speed Vm from the stop position S1, and travels at a constant time interval using a periodic function with a cycle Tm, an amplitude center of Vm, and an amplitude of Wm. Generate a pattern. If a sine wave is used for the periodic function as shown in FIG. 14, a running pattern with a continuous acceleration change can be generated. Moreover, if a triangular wave is used, the calculation load at the time of driving | running | working pattern production | generation can be reduced.
[0139]
As for deceleration, when the remaining distance to the next stop position S2 becomes smaller than the distance necessary to stop at the acceleration Dm from the current speed V (i), that is, when the following expression is satisfied, the speed is zero. Decelerate with acceleration Dm until [km / h].
[0140]
## EQU11 ##
L−∫V (n) dt ≦ V (i)²/ (2 | Dm |) Formula 13
[0141]
Here, L [m] is the distance from the stop position S1 to S2, and ∫V (n) [m] is the travel distance from S1.
[0142]
In step 19, if the target section for predicting the travel pattern is the last section on the route to the destination, the present control is terminated. If not, the process returns to step 14 to predict the travel pattern of the next section.
[0143]
The above is the basic control procedure of this example, but the control contents of step 16 and step 17 can be changed as follows.
[0144]
First, in step 16, the acceleration / deceleration period during traveling is determined according to the signal interval (Sim [m]) obtained by dividing the length of the section divided in step 13 by the acceleration / deceleration period predicting means 109 by the number of signals in the section. Tm is corrected. In step 17, the acceleration / deceleration amplitude prediction means 110 corrects the velocity amplitude Wm in acceleration / deceleration during traveling according to the signal interval Sim as in step 16. Such a correction value of the period Tm and the amplitude Wm can be obtained from a relational expression as shown in FIG. 15, for example. The relational expression in FIG. 15 desirably reflects the tendency obtained as a result of analyzing actual traveling data, for example, the shorter the signal interval, the shorter the acceleration / deceleration period during traveling and the smaller the amplitude of speed. .
[0145]
As another modification, in step 16, the acceleration / deceleration period Tm is corrected by the acceleration / deceleration period predicting means 109 according to the road type for each section divided in step 13. In step 17, the acceleration / deceleration amplitude prediction means 110 corrects the velocity amplitude Wm in acceleration / deceleration during traveling according to the road type as in step 16. The correction value of the acceleration / deceleration period and speed amplitude during traveling is calculated based on actual traveling data for each road type such as an expressway abbreviation, general road (1 lane), and general road (2 lane). It is desirable to extract the velocity amplitude and determine the average value thereof.
[0146]
As another modified example, in step 16, the acceleration / deceleration period predicting unit 109 obtains the congestion degree information (average traveling speed :) for each section divided in step 13 obtained from the infrastructure such as VICS by the traffic information detecting unit 102. The acceleration / deceleration period Tm during traveling is corrected according to TDm [km / h]). In step 17, the acceleration / deceleration amplitude prediction means 110 corrects the speed amplitude Wm in acceleration / deceleration during traveling according to the degree of congestion (average traveling speed: TDm) as in step 16. Such a correction value of the period Tm and the amplitude Wm can be obtained from a relational expression as shown in FIG. 16, for example. The relational expression in FIG. 16 desirably reflects the tendency obtained as a result of analyzing actual traveling data, for example, the higher the congestion degree, the shorter the acceleration / deceleration period during traveling and the greater the speed amplitude. .
[0147]
Thus, by appropriately correcting the acceleration / deceleration period and amplitude according to the signal interval, road type, or congestion level, the obtained travel pattern can be made closer to actual travel.
[0148]
Fourth embodiment
Next, a fourth embodiment of the present invention will be described. The control contents of the fourth embodiment are basically the same as those of the third embodiment described above, but the running pattern during running is recorded for each section divided at step 13 in FIG. , And the predicted value of the acceleration / deceleration period and amplitude during traveling are sequentially corrected by learning according to the road type and signal interval of the section.
[0149]
FIG. 17 is a flowchart showing the control procedure of this example. First, at step 21, the traveling speed V (i) is recorded. The travel speed may be recorded at regular time intervals as shown in FIG. 18, for example, or may be performed for each node or complementary point representing the road coordinates recorded in the map data.
[0150]
In step 22, if the travel position is the end of the section, the process proceeds to step 23, and the calculation of the acceleration / deceleration period and amplitude during travel is started from the travel pattern of the section traveled so far. Go back to and continue to record the running speed.
[0151]
In step 23, the difference V (i) −V (i−1) of the traveling speed is calculated for the traveling pattern of the section that has been traveled so far recorded in step 21.
[0152]
In step 24, as shown in FIG. 18, the travel pattern of the section that has been traveled so far is divided into sections having the same sign of the difference calculated in step 23, that is, an acceleration or deceleration continuation section.
[0153]
In step 25, the acceleration or deceleration continuation section divided in step 24 is removed from the portion where the speed is equal to or lower than a constant value Vs (for example, 15 [km / h]), thereby starting acceleration from the stop. And excluding deceleration to stop. In the example of FIG. 18, the section from V (1) to V (2) and the section from V (8) to V (10) are excluded.
[0154]
In step 26, the period of the continuous part other than the part removed in step 25 is calculated. For the calculation of the period, there is a method in which the time for repeating “acceleration → deceleration” or “deceleration → acceleration” is obtained and set as the period Tm. When there are two or more cycles of acceleration / deceleration in the section, the average value thereof is taken. In the example of FIG. 18, the average of T (4) -T (2) and T (8) -T (4) is set as the acceleration / deceleration period during traveling. Also, a method of averaging a value obtained by doubling the length of each acceleration or deceleration continuation section to obtain a period Tm can be employed. In the example of FIG. 10, the following equation is obtained.
[0155]
[Expression 12]
Tm = 2 × {(T (3) −T (2)) + (T (4) −T (3))
+ (T (6) -T (4)) + (T (8) -T (6))} / 4
[0156]
In step 27, the absolute value of the speed change amount in each acceleration or deceleration continuation section other than the portion removed in step 25 is obtained, and the average value is set as the amplitude Vm. In the example of FIG. 18, the amplitude is expressed by the following equation.
[0157]
[Formula 13]
Wm = (| V (2) −V (3) | + | V (3) −V (4) |
+ | V (4) -V (6) | + | V (6) -V (8) |) / 4
[0158]
In step 28, the predicted values so far are corrected using the period Tm and the amplitude Wm obtained in steps 26 and 27. By doing in this way, the period and amplitude of acceleration / deceleration during traveling according to the driving tendency of the driver can be predicted. Examples of the correction include a method using a weighted average with the predicted values TM (n) and WM (n) (n is the number of corrections so far for each road type) so far.
[0159]
[Expression 14]
TM (n + 1) = (TM (n) + Tm) / 2
WM (n + 1) = (WM (n) + Wm) / 2
[0160]
Alternatively, there is a method in which the number n of corrections so far for each road type is recorded and a weighted average is taken using the number n of corrections as follows.
[0161]
[Expression 15]
TM (n + 1) = (n × TM (n) + Tm) / (n + 1)
WM (n + 1) = (m × WM (n) + Wm) / (n + 1)
[0162]
Alternatively, as described below, there is a method of recording predicted values up to a certain number of times in the past and using an average value thereof.
[0163]
[Expression 16]
TM (n + 1)
= {TM (n-p) + ... + Tm (n-1) + Tm (n)} / (p + 1)
WM (n + 1)
= {WM (n-p) + ... + Wm (n-1) + Wm (n)} / (p + 1)
[0164]
Note that the acceleration / deceleration period and amplitude prediction value during travel are recorded for each road type. In the example of FIG. 18, the predicted value for a general road (two lanes) is corrected.
[0165]
In step 29, if the target section for correction of the acceleration / deceleration period and amplitude during traveling is the last section on the route to the target, the present control is terminated. Otherwise, the process returns to step 14 to correct the next section. I do.
[0166]
Fifth embodiment
FIGS. 19 to 21 show still another embodiment of the present invention. The control content of the fifth embodiment is basically the same as that of the third embodiment described above, but traveling pattern prediction is performed. If there is a curve in the section to be performed, the acceleration / deceleration amplitude during traveling is corrected to zero. In addition to this, a method may be adopted in which the acceleration / deceleration period is long and the amplitude is small when traveling in a curve section. This control content is a subroutine called from step 18 in FIG.
[0167]
First, at step 211, if the curvature r (i) at the point I in the section where the running pattern is predicted is equal to or less than a set value α (for example, 1/50), the curve section is regarded as proceeding to step 212. Otherwise, the process proceeds to step 215, and as shown in FIG. 20, acceleration / deceleration is added to the travel pattern during travel, as in the process of step 18 of FIG.
[0168]
In step 212, the curve passing speed Vc is obtained from the curvature r (i). As a relational expression between the curvature and the curve passing speed, for example, a monotone decreasing function as shown in FIG. 21 can be used.
[0169]
In step 213, as shown in FIG. 20, the speed V (i) at point I in the section where the running pattern is predicted is set to Vc obtained in step 212. By doing in this way, the driving | running | working pattern which drive | works at the fixed speed Vc is generated from after the curve approach to before the exit as shown in FIG.
[0170]
In step 214, if the travel position is at the end of the section, the present control is terminated. If not, the process returns to step 211 to continue generating the travel pattern.
[0171]
As described above, in the travel pattern generation device according to the third to fifth embodiments, the travel pattern is generated with the periodic function based on the acceleration / deceleration period, amplitude, and center of amplitude during travel. The driving pattern to the destination including can be accurately predicted. As a result, in the conventional travel pattern generation device, as shown in FIG. 22 (a), the predicted pattern has poor consistency with the actual travel pattern, and the fuel consumption prediction reliability is lacking. In the pattern generation device, as shown in FIG. 5B, the predicted pattern and the actual running pattern are in good agreement, so the prediction reliability such as fuel consumption is significantly improved.
[0172]
Other embodiments
Incidentally, as an example of using the travel pattern on the route to the destination obtained in the embodiment of the present invention, it is provided in the HEV (hybrid vehicle) shown in FIG. 23 and the FCV (fuel cell electric vehicle) shown in FIG. The determination of the charging / discharging schedule of a battery is mentioned.
[0173]
Hereinafter, an example of a method for determining a charging / discharging schedule of energy on the route to the destination will be described using the traveling pattern predicted by the above procedure.
[0174]
The outline of the charging / discharging schedule determination method of this example is executed according to the following process as shown in FIG.
[0175]
First, using the predicted traveling pattern, an output necessary for traveling on the route to the destination is calculated. Then, the output necessary for traveling becomes negative, that is, the section where energy regeneration is possible is divided into several scheduling sections.
[0176]
Further, the value of the battery charge amount SOC at the end of the scheduling period is adjusted so that the regenerative energy in the next scheduling period can be charged to the maximum. For that purpose, motor driving in the scheduling interval is considered.
[0177]
Further, when the value P (SOCmax−SOCp) (w) obtained by converting SOCmax−SOCp into the output is smaller than the energy C (v) that can be regenerated in the next scheduling section, the motor travels from the section where the engine operating efficiency is low. I do. (However, SOCmax is the upper limit value of the battery charge amount, and SOCp is the battery charge amount at the head of the scheduling interval.)
Further, when the value P (SOCmax−SOCp) (w) obtained by converting SOCmax−SOCp into the output is larger than the energy C (w) that can be regenerated in the next scheduling section, the case where the engine runs alone and the motor running Therefore, the fuel consumption when power generation is performed in advance is compared, and the charge / discharge schedule is determined so as to perform the smaller one.
[0178]
Based on the basic idea as described above, the flow of charge / discharge schedule determination will be described according to the flowchart of FIG.
[0179]
First, in step Sa1, it is searched whether there is a section on the route to the destination where energy can be continuously recovered for a certain time (for example, 1 minute), and if there is, the process proceeds to step Sa2. Jump to Sa6. For example, in the route shown in FIG. 25, the downhill on the general road from the deceleration before the exit toll gate of the expressway corresponds to this section.
[0180]
In step Sa2, the route to the destination is divided into scheduling intervals by the interval in which energy can be continuously recovered searched in step Sa1. In the example of FIG. 25, the vehicle is divided into two scheduling sections with a deceleration before the exit toll gate on the expressway as a boundary. In addition, the process after step Sa3 is performed in order from the scheduling area by the side of departure.
[0181]
In step Sa3, if the battery charge amount SOCp at the head of the scheduling interval is near the lower limit SOCmin, the process jumps to step Sa6, and if not, the process proceeds to step Sa4.
[0182]
In step Sa4, when the battery charge SOCp at the beginning of the scheduling interval exceeds the upper limit SOCmax during energy regeneration, that is, a value P (SOCmax-SOCp) (v) obtained by converting SOCmax-SOCp into an output is regenerated. If it is smaller than the possible energy C (v), the process proceeds to step Sa5, and if not, the process jumps to step Sa6. In the example of FIG. 25, if the battery charge amount is the value SOCp at the head of the scheduling interval 1, all the regenerative energy C (w) in the scheduling interval 2 can be charged, and the process proceeds to step Sa6.
[0183]
In step Sa5, the charging / discharging schedule is determined so that the battery is discharged by running or assisting in the section where the engine operating point is farthest from the best efficiency line. In the example of FIG. 25, a traffic jam section in an urban area corresponds.
[0184]
In step Sa6, if the operating point of the engine is away from the best efficiency line, that is, there is a section where the operating efficiency of the engine is low, the process proceeds to step Sa7, and if not, the process jumps to step Sa8. In the example of FIG. 25, a traffic jam section in an urban area corresponds.
[0185]
In step Sa7, the fuel consumption amount B when the engine travels in the section where the operating efficiency of the engine detected in step Sa6 is low, or when the value of the battery charge SOC can be traveled by the motor is subtracted. And the fuel consumption A in the case where power is generated in advance to charge the battery with the energy for running the motor in the section. If the fuel consumption A is less than B, power is generated in advance. In the example of FIG. 25, the fuel consumption A when the energy for driving the traffic in the urban area on the previous general road is generated more than the fuel consumption B in the case where the traffic in the urban area of the scheduling section 1 is driven only by the engine. Therefore, the charging / discharging schedule is determined so that power is generated on a general road and the battery is consumed by running the motor in a traffic jam in an urban area. The amount of power generation is set so that energy can be regenerated when stopped at a traffic light.
[0186]
In step Sa8, if the scheduling interval to be processed next is the final interval, the process proceeds to step Sa9. Otherwise, the process returns to step Sa3.
[0187]
In step Sa9, the charging / discharging schedule is determined so that the battery is consumed by running or assisting the motor in the section where the engine operating point is farthest from the best efficiency line. In the example of FIG. 25, since energy is regenerated in the final scheduling section, there is only a general road uphill, so the battery is consumed by assisting the motor in this section.
[0188]
The above is an example of a method for determining the charge / discharge schedule of the battery.
[0189]
As described above, the energy consumption amount on the route to the destination can be strictly calculated by using the prediction result of the travel pattern in consideration of the stop / start on the route to the destination.
[0190]
Further, by using the prediction result of the travel pattern in consideration of the stop / start on the route to the destination, the energy accumulation and release schedule on the route to the destination can be determined strictly.
[0191]
The travel pattern obtained by the travel pattern generation device according to the third to fifth embodiments of the present invention can also be applied to the above-described energy charge / discharge schedule determination method. That is, the operating efficiency of the engine is obtained from the traveling load, the battery is charged in a section with high operating efficiency, and the battery charge / discharge schedule is generated so that the motor travels in a section with low operating efficiency.
[0192]
The outline of the battery charge / discharge scheduling of HEV will be described. As shown in FIG. 27, first, a traveling speed pattern to the destination is generated by the method described in the description of the flowchart of FIG. Next, using the road gradient and travel speed pattern of the route to the destination, a travel load pattern to the destination is obtained by combining the slope resistance, the travel resistance, and the acceleration resistance. Then, the engine / motor driving force distribution is determined so as to reduce the fuel consumption while satisfying the travel load pattern to the destination and the target SOC (battery charging rate) at the destination. Finally, a charge / discharge schedule is determined by determining that the SOC pattern from the driving force distribution to the destination is within the range of the upper limit value and the lower limit value. In the example of FIG. 27, in low-speed driving in urban areas, the driving load is small and the engine operating efficiency is low, so motor driving is given priority. A signal with a large driving load negative area, deceleration before an intersection, and energy in downhill sections The charging / discharging schedule is determined so that the motor is assisted by the motor using the energy charged by the regeneration in the uphill after the downhill. By using the travel pattern generated in consideration of acceleration / deceleration during traveling in this way, it is possible to accurately predict the regeneration amount and torque required for acceleration due to deceleration during traveling. It can be performed well and fuel efficiency is improved. This application example can be applied not only to HEVs but also to EVs (electric vehicles) and FCVs. In that case, signals and deceleration before a crossing or before a section where energy regeneration is possible in a downhill section, etc. Then, the battery is discharged, and the battery is charged before a section where a large motor torque such as uphill is required.
[0193]
The travel pattern generated by the travel pattern generation device of the present invention can be used to predict the total amount of fuel consumed up to the destination by using the engine operation efficiency map and the fuel consumption rate map.
[0194]
FIG. 28 is a flowchart showing a procedure for predicting the total amount of fuel consumed up to the destination.
[0195]
First, in step 311, the traveling load P (i) [w] is obtained from each sample value V (i) of the speed pattern. The following relational expression is used for the calculation of the running load.
[0196]
[Expression 17]
P (i) = {Rr + Ra (i) + Rc (i) + Rg (i)} × V (i)
However, Rr = μMg, Ra = acc (i) M,
Rc = 0.5ρCdAV (i)²
Rg = Mgsin {atan (Gr (i) / 100)}
[0197]
Here, M (kg) is the vehicle weight, μ is the rolling resistance coefficient, and ρ (kg / m³) Is the air density, Cd is the air resistance coefficient, and A (m²) Is the foreshadow projection area, g (m / s)²) Is the gravitational acceleration, acc (m / s²) Is the acceleration of the vehicle, and Gr (i) (%) is the calculated value of the road gradient.
[0198]
In step 312, as shown in FIG. 29, the operating point related in the hybrid control unit for the work of the traveling load P (i) obtained in step 311 is read out, and the fuel at this time is read from the fuel consumption rate map of the engine. The consumption rate f (i) [cc / sec] is obtained.
[0199]
In step 313, if the sample value V (i) is the final sample of the travel pattern on the route to the destination, it is determined that the calculation of the fuel consumption rate for each sample value of speed has been completed to the destination, and the flow proceeds to step 314. If not, return to Step 311.
[0200]
In step 314, the total fuel consumption F [cc] is obtained by integrating the fuel consumption rate f (i) obtained in step 312 from the starting point to the destination as described below.
[0201]
[Expression 18]
F = ∫V (i) dt
[0202]
As an application example of the fuel consumption prediction to the destination, as shown in FIG. 30, when the destination is set by the navigation device, the fuel cost per unit amount is calculated as the fuel consumption obtained for a plurality of route candidates. A system that multiplies and presents fuel costs can be considered. This control can be applied not only to HEV but also to fuel consumption prediction of gasoline vehicles and diesel vehicles.
[0203]
Although the embodiments of the present invention have been described above, the present invention is not only used for determining the charging / discharging scale of a hybrid vehicle or FCV battery, but also used for livestock heat and heat radiation scheduling of an air conditioner equipped with a livestock heat accumulator. Or, it may be applied to a more accurate fuel consumption calculation up to the destination in consideration of the energy balance accompanying the stop and start. Further, the present invention is not limited to the above embodiment, and any one of them may be combined.
[Brief description of the drawings]
FIG. 1 is a block diagram showing a first embodiment of a travel pattern generation device of the present invention.
FIG. 2 is a diagram illustrating an example of a scheduled travel route.
FIG. 3 is a graph showing an example of the number of stops with respect to an average signal interval.
FIG. 4 is a graph showing an example of a traveling speed with respect to a curvature radius of a road.
FIG. 5 is a pattern diagram showing an example of a travel pattern generated by the travel pattern generation device of the present invention.
FIG. 6 is a graph obtained by converting the travel pattern shown in FIG. 5 into a travel pattern with respect to time.
FIG. 7 is a flowchart showing a control procedure of the travel pattern generation device of the present invention.
FIG. 8 is a flowchart showing a control procedure according to the second embodiment of the present invention.
FIG. 9 is a pattern diagram showing a running pattern of the embodiment shown in FIG.
FIG. 10 is a block diagram showing a third embodiment to a fifth embodiment of the travel pattern generation device of the present invention.
11 is a block diagram further embodying the embodiment shown in FIG.
FIG. 12 is a flowchart showing a control procedure according to the third embodiment of the present invention.
FIG. 13 is a control map showing the relationship between road type, travel speed, and acceleration / deceleration.
14 is a graph showing a running pattern generated by the running pattern generation device shown in FIG.
FIG. 15 is a graph showing the relationship between signal interval, acceleration / deceleration period, and amplitude.
FIG. 16 is a graph showing the relationship between the degree of congestion, acceleration / deceleration period, and amplitude.
FIG. 17 is a flowchart showing a control procedure according to the fourth embodiment of the present invention.
FIG. 18 is an example of a travel pattern for explaining a control procedure of the fourth embodiment.
FIG. 19 is a flowchart showing a control procedure according to the fifth embodiment of the present invention.
FIG. 20 is an example of a running pattern for explaining a control procedure of the fifth embodiment.
FIG. 21 is a graph showing the relationship between curve passing speed and curvature.
FIG. 22 is a diagram showing a traveling pattern obtained in the present invention and a conventional traveling pattern.
FIG. 23 is a block diagram showing an example of a hybrid vehicle to which the traveling pattern generation device of the present invention is applied.
FIG. 24 is a block diagram showing an example of a fuel cell electric vehicle to which the traveling pattern generation device of the present invention is applied.
FIG. 25 is a pattern diagram showing an example in which the traveling pattern generated by the present invention is applied to a hybrid vehicle.
26 is a flowchart showing a control procedure of the embodiment shown in FIG. 25. FIG.
FIG. 27 is a pattern diagram showing another example in which the traveling pattern generated by the present invention is applied to a hybrid vehicle.
FIG. 28 is a flowchart showing a procedure for obtaining a fuel consumption amount using a running pattern obtained in the present invention.
FIG. 29 is a graph showing the relationship between engine speed and engine output torque.
30 is a diagram showing an example in which the embodiment shown in FIG. 28 is applied to a navigation device.
[Explanation of symbols]
1 ... Traveling pattern generation device
11 ... Road information acquisition means
13 ... Stop count prediction means
14: Traveling speed prediction means
15 ... Acceleration / deceleration prediction means
16 ... Traveling pattern generation means

Claims (19)

In a travel pattern generation device that has road information acquisition means for acquiring information related to a planned travel route of the host vehicle, and that generates a travel pattern of the host vehicle in the planned travel route using the information acquired by the road information acquisition unit,
Route classifying means for classifying the planned travel route based on the information acquired by the road information acquiring means;
Travel speed prediction means for predicting the travel speed of the host vehicle based on road information in the section divided by the route classification means;
Acceleration / deceleration period predicting means for predicting an acceleration / deceleration period during traveling in the section divided by the route dividing means;
Acceleration / deceleration amplitude prediction means for predicting acceleration / deceleration amplitude during traveling in the section divided by the route classification means;
While using a periodic function based on the cycle obtained by the acceleration / deceleration cycle predicting means, the amplitude obtained by the acceleration / deceleration amplitude predicting means, and the center of the amplitude obtained by the running speed predicting means, Traveling pattern generation means for generating a speed pattern waveform of
A travel pattern generation device further comprising: Further comprising signal interval calculation means for determining the signal interval by dividing the length of the section divided by the route dividing means by the number of signals present in the section;
The acceleration / deceleration cycle predicting unit corrects the acceleration / deceleration cycle during traveling according to the signal interval obtained by the signal interval calculation unit,
The deceleration amplitude predicting means, running pattern generating apparatus according to claim 1, wherein correcting the acceleration and deceleration amplitude traveling in response to a signal interval obtained by the signal interval calculating means. Road type detecting means for detecting the road type of each section divided by the route dividing means,
The acceleration / deceleration cycle prediction unit corrects the acceleration / deceleration cycle during traveling according to the road type obtained by the road type detection unit,
The deceleration amplitude predicting means, running pattern generating apparatus according to claim 1, wherein correcting the acceleration and deceleration amplitude during traveling depending on the road type obtained by the road type detecting means. Further comprising congestion degree detecting means for detecting the congestion degree of each section divided by the route dividing means,
The acceleration / deceleration cycle predicting unit corrects the acceleration / deceleration cycle during traveling according to the congestion level obtained by the congestion level detection unit,
The deceleration amplitude predicting means, running pattern generating apparatus according to claim 1, wherein correcting the acceleration and deceleration amplitude traveling in accordance with the congestion degrees obtained by the congestion degree detecting means. A signal interval calculation means for obtaining a signal interval by dividing the length of the section divided by the path dividing means by the number of signals existing in the section;
Road type detection means for detecting the road type of each section divided by the route classification means;
A travel speed detecting means for detecting the travel speed of the host vehicle;
The acceleration / deceleration cycle predicting means uses the traveling speed obtained by the traveling speed detecting means, and according to the signal interval obtained by the signal interval calculating means and the road type obtained by the road type detecting means, Learn the acceleration / deceleration period excluding start acceleration from stop and deceleration to stop, and correct the predicted value,
The acceleration / deceleration amplitude predicting means uses the traveling speed obtained by the traveling speed detecting means, and according to the signal interval obtained by the signal interval calculating means and the road type obtained by the road type detecting means, start acceleration and running pattern generating apparatus according to claim 1, wherein the correcting the predicted value by learning the amplitude of the acceleration and deceleration except the deceleration and to stop from the stop. A curve detecting means for detecting a curve of each section divided by the route dividing means;
The acceleration / deceleration period predicting means corrects the acceleration / deceleration period during running in the curve obtained by the curve detecting means, and the acceleration / deceleration amplitude predicting means is running in the curve obtained by the curve detecting means. running pattern generating apparatus according to claim 1, wherein correcting the acceleration and deceleration amplitude. Based on the travel pattern of the vehicle generated by the running pattern generating means, generating running pattern according to claim 1, further comprising an energy consumption amount calculation means for calculating the energy consumption of the vehicle in the traveling route to the destination apparatus. Energy balance schedule calculation means for calculating energy storage and release schedules for the energy storage means of the host vehicle in the travel route to the destination based on the travel pattern of the host vehicle generated by the travel pattern generation means. The travel pattern generation device according to claim 1 . The travel pattern generation means includes
In sections it is partitioned by the path segment means, to generate a pattern from the stop position reaches a predetermined speed, the <br/> claim generates the speed pattern waveform in the running from the time it reaches the predetermined speed 1 The travel pattern generation device described. The periodic function is a sine wave or running pattern generating apparatus of <br/> recited in claim 1, including a triangular wave. Further comprising stop number prediction means for predicting the number of stops of the host vehicle in the planned travel route using the information acquired by the road information acquisition means,
The travel pattern generation device according to claim 1 , wherein the travel pattern generation unit generates the speed pattern between the stop positions of the host vehicle based on the number of stops predicted by the stop number prediction unit. . The stop number prediction means includes
First stop number prediction means for detecting the position of an intersection and a traffic light in the planned travel route of the host vehicle acquired by the road information acquisition means;
Dividing the planned travel route of the host vehicle into several sections, obtaining an average interval between intersections and traffic lights existing in each section, and predicting the number of stops of the host vehicle in each section based on the average interval The travel pattern generation device according to claim 11 , further comprising: 2 stop count prediction means. The travel pattern generation device according to claim 12, wherein the second stop count prediction means varies the stop count according to a travel time zone. The travel pattern generation device according to claim 12, wherein the second stop number prediction means predicts that the vehicle stops at an intersection where the host vehicle bends in a direction crossing the opposite lane on the planned travel route. The travel according to claim 12, wherein the second stop number predicting means predicts that the vehicle stops at an intersection that crosses a road having a higher priority than the host vehicle or a right or left turn on a road having a higher priority than the host vehicle. Pattern generator. The travel pattern generation device according to claim 12, wherein the second stop number prediction means predicts that the vehicle stops at an intersection with a road having a higher priority than the host vehicle in the planned travel route. The travel pattern generation device according to claim 12, wherein the second stop count prediction means predicts that a stop is made at a toll gate on the planned travel route. It further comprises a travel history storage means for storing a past travel history,
It said second number of stops predicting means includes sequentially from the travel history intersection ratio of the number of stops is large with respect to the travel times based on the travel history storing means past which is stored in or signals, according to claim 12, wherein selecting the stop location Travel pattern generation device. The travel pattern generation device according to any one of claims 1 to 18, wherein when the traffic situation on the planned travel route changes, the travel pattern is generated again based on the information after the change.

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